Scheduling for wireless control in single hop WirelessHART ...570078/FULLTEXT01.pdf · 1.1.3 Chapter 4:Automation System 800xA In this chapter the industrial IT System 800XA process

University of L’AquilaFaculty of Engineering

Master’s Degree in Electronics

Engineering

Scheduling for wireless control

in single hop WirelessHART networks

Supervisors: Authors:

M.D. Di Benedetto Valeria Ercoli

Alf Isaksson Identification Number:

Karl Henrik Johansson 171448

A.A. 2009/2010

University of L’AquilaFaculty of Engineering

Master’s Degree in Electronics Engineering

Scheduling for wireless control

in single hop WirelessHART networks

Supervisors: Authors:

M.D. Di Benedetto Valeria Ercoli

Alf Isaksson Identification Number:

Karl Henrik Johansson 171448

A.A. 2009/2010

To my Dad

Acknowledgement

I would like to thank some of the people who made this work possible, feasible and plea-

surable.

A special gratitude to Alf Isaksson, my supervisor at ABB, for his kindness, patience,

motivation, enthusiasm, and immense knowledge.

I would like to express my thanks to Karl Henrik Johansson, my supervisor at KTH,

for his detailed review and constructive comments, and for having involved me in this

interesting project.

I wish to express my warm and sincere thanks to my home university supervisor Maria

Domenica Di Benedetto. I will always be greatly indebted to her for providing me with

the stimulating opportunity to make my master thesis in Sweden.

I am especially grateful to Dr.Alessandro D’Innocenzo for his constant support and

encouragement during these years.

Finally, I would like to thank my family and friends for giving me happiness and joy

during my difficult moments.

ii

Abstract

Key words : Automatic process control, WirelessHART, controlled variables, con-

troller, fieldbus, network reliability, performance analysis, PID control, process automa-

tion, scheduling algorithms.

iii

Contents

1 Introduction 1

1.1 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.1.1 Chapter 2:WirelessHART . . . . . . . . . . . . . . . . . . . . . . . . 2

1.1.2 Chapter 3:Scheduling Theory . . . . . . . . . . . . . . . . . . . . . . 3

1.1.3 Chapter 4:Automation System 800xA . . . . . . . . . . . . . . . . . 3

1.1.4 Chapter 5:Process Control . . . . . . . . . . . . . . . . . . . . . . . . 3

1.1.5 Chapter 6:Scheduling of Wireless Control . . . . . . . . . . . . . . . 3

1.1.6 Chapter 7:Conclusions and future work . . . . . . . . . . . . . . . . 3

2 WirelessHART 4

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 Wireless Standard for Industrial Automation . . . . . . . . . . . . . . . . . 4

2.3 WirelessHART Standard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.3.1 TDMA Data Link Layer . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3.2 WirelessHART Gateway . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3.3 WirelessHART Network Manager . . . . . . . . . . . . . . . . . . . . 9

2.3.4 WirelessHART Network Schedule . . . . . . . . . . . . . . . . . . . . 10

2.3.5 Schedule Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.3.6 Communication Tables . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.3.7 Graph Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3 Scheduling Theory 17

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.2 Scheduling Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.2.1 On-line scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.2.2 Off-line scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

i

CONTENTS

4 Automation System 800xA 22

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2 AC800M Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.3 Evolution of Control Technology . . . . . . . . . . . . . . . . . . . . . . . . 24

4.4 Clock synchronization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.5 Fieldbus Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.6 S800 I/O . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.6.1 S800 I/O Station Data Scanning . . . . . . . . . . . . . . . . . . . . 33

5 Process Control 35

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.2 PID Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.2.1 Proportional Action . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5.2.2 Integral Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

5.2.3 Derivative Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

5.3 Cascade Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

5.4 Mid-Range Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.5 Split-Range Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

5.6 Ratio Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

6 Scheduling of Wireless Control 42

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

6.2 Scheduling problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . 44

6.2.1 Superframes setting . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

6.2.2 Communication and control superframe schedule . . . . . . . . . . . 46

6.3 Formalization of the scheduling problem . . . . . . . . . . . . . . . . . . . . 47

6.4 Scheduling policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6.4.1 Scenario I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6.4.2 Scenario II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

6.4.3 Shared Slots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

6.5 Boliden Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

7 Conclusions and future works 66

A Boliden Control Variables 68

ii

CONTENTS

Bibliography 71

iii

List of Figures

2.1 WirelessHART network components. . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Slot timing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3 Communication tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.4 Network topologies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.1 800xA system network architecture. . . . . . . . . . . . . . . . . . . . . . . 23

4.2 S800 I/O station overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.3 S800 I/O Dynamic Data Exchange. . . . . . . . . . . . . . . . . . . . . . . . 32

6.1 Industrial network topology [13]. . . . . . . . . . . . . . . . . . . . . . . . . 44

6.2 PID controller. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

6.3 Example of a generic control loop. . . . . . . . . . . . . . . . . . . . . . . . 46

6.4 Set of control loops Example I.1. . . . . . . . . . . . . . . . . . . . . . . . . 52

6.5 Superframes Example I.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55





6.10 Set of control loops Example II.1. . . . . . . . . . . . . . . . . . . . . . . . . 58

6.11 Precedence graph Example II.1. . . . . . . . . . . . . . . . . . . . . . . . . . 58

6.12 Superframes Example II.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

6.13 Set of control loops Example II.2. . . . . . . . . . . . . . . . . . . . . . . . . 59

6.14 Precedence graph Example II.2. . . . . . . . . . . . . . . . . . . . . . . . . . 59

6.15 Superframes Example II.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

6.16 Set of control loops Example Shared Slots. . . . . . . . . . . . . . . . . . . . 61

6.17 Superframes Example Shared Slots. . . . . . . . . . . . . . . . . . . . . . . . 62

iv

LIST OF FIGURES

6.18 Diagram of froth flotation cell. . . . . . . . . . . . . . . . . . . . . . . . . . 63

6.19 Zinc flotation circuit [24]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

v

List of Tables

A.1 Controlled variables for the Garpenberg plant. . . . . . . . . . . . . . . . . 69

A.2 Garpenberg plant: variables to be scheduled in shared slots. . . . . . . . . . 70

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Chapter 1

Introduction

This thesis is part of the SOCRADES project, a European research and advanced devel-

opment project with the primary objective to create new methodologies, technologies and

tools for the modeling, design, implementation and operation of networked control sys-

tems embedded in smart physical objects. These systems are becoming more important

in the new-generation industrial automation field thanks to the many advantages intro-

duced by the networks. In fact, the use of a network to connect the devices permits to

eliminate unnecessary wirings, reducing the complexity and the overall cost in designing

and implementing the control systems. In the last years the fast spread of the wireless

technologies has opened new scenarios for the communication in the automation field.

The benefits introduced by the use of wireless communication in the networked control

system are many,first of all the gain in productivity and flexibility,and of course the sim-

plicity and the convenience of the sensors placement. Wireless industrial communications

based on WLAN and IEEE 802.15 standards are in the focus of this kind of research and

development. In particular, wireless sensor/actuator networks (WSN) are to be closely

investigated, as they will definitely foster the mobility and flexibility required in industrial

communication. The natural features of wireless technologies enable greater opportunities

for reconfiguration/upgrading, maintenance and fault tolerance. An industrial application,

as the one considered in this work, will frequently require hard bounds on the maximum

delay allowed. In particular, as the sensors and actuators are part of closed loop control

systems, strict timing requirements apply, ensuring a short response time and an efficient

use of the available radio bandwidth. Thus, algorithms and software that are capable of

dealing with hard and soft time constraints are very important in control implementation

1

1.1. Outline of the thesis

and design, and areas such as real-time systems from computer science are becoming in-

creasingly important also in control theory. This combined with the trend of having more

functionality being realized in software, make resource scheduling and its effect on control

performance a relevant issue.

This work is focused on the study of the problem of finding a good scheduling algorithm

in order to manage the exchange of information between sensors/actuators and the gateway

and between the gateway and the controller in a WirelessHART networked control system.

WirelessHART is a wireless protocol that provides a low cost, relatively low speed (e.g.,

compared to IEEE 802.11g) wireless connection. The WirelessHART standard does not

give any specification concerning about a particular scheduling algorithm to be used in

a WirelessHART network. However, in this Standard there are some requirements to

be taken into account. The scheduling theory has been deeply explored in the academic

literature and has progressed in recent years due to the strict requirements of real-time

systems such as predictability and timing constraints. However, extending this scheduling

theory in practice is not so easy cause these theoretical approaches to schedule do not

fit well with a real environment in which the notion of task is often completely different

from the one defined in theoretical algorithms. In these cases the implementation of a new

scheduling algorithm is required.

In this thesis a heuristic, off-line algorithm, priority based is proposed and described in

deep with some meaningful examples. The suggested scheduling policy has been applied to

two different ideal scenarios. The last part of the thesis deals with a level control problem

in a mineral flotation plant and with the possibility to use a wirelessHART network for

that plant.The proposed algorithm is applied also to this industrial environment and it is

proved to be a good solution to meet the feasibility-delay tradeoff.

Some relevant considerations and conclusions follows.

1.1 Outline of the thesis

1.1.1 Chapter 2:WirelessHART

In this chapter the WirelessHART protocol will be described. The first two sections

give a general introduction to the protocol with the main information and the technical

characteristics of WirelessHART. In the third section a more exhaustive description of

the communication protocol is given: the structure of the MAC protocol (Medium Access

2

1.1. Outline of the thesis

Control),the devices and the network resources specification and in particular scheduling

and routing are deeply explained.

1.1.2 Chapter 3:Scheduling Theory

This chapter is an overview of the basic scheduling algorithms of the academic literature

suitable for sensor networks. Both on-line and offline algorithms are analyzed.

1.1.3 Chapter 4:Automation System 800xA

In this chapter the industrial IT System 800XA process automation system is described.In

particular AC800M controller is presented since it is the most current controller series used

within all of the ABB and it provides modern communication features.

1.1.4 Chapter 5:Process Control

This chapter will deal with components required to build complex automation systems

using the bottom up approach. The key component is the PID controller and it will

be described in Section 5.2. Other important control principles such as cascade control,

mid-range control, split-range control and ratio control will be discussed, respectively, in

Sections 5.3, 5.4, 5.5, 5.6.

1.1.5 Chapter 6:Scheduling of Wireless Control

In this chapter is presented the proposed scheduling algorithm.The performance of the

algorithm are evaluated from the viewpoint of the feasibility and delay by simulation on

several ideal scenarios.The proposed approach is applied also to a real industrial environ-

ment that is Garpenberg mineral flotation plant.

1.1.6 Chapter 7:Conclusions and future work

The work concludes with a statement of future work.

3

Chapter 2

WirelessHART

2.1 Introduction

NOTE: IN THIS CHAPTER...

2.2 Wireless Standard for Industrial Automation

Wireless technologies give several advantages to industrial automation in terms of gain in

productivity and flexibility. Industrial sites are often harsh environments with stringent

requirements on the type and quality of cabling. Moreover they can easily require many

thousands of cables and it could be difficult to engineer additional wires in an already

congested site. Thus wireless communication can save costs and time. At the same time

it improves reliability with respect to wired solutions by means of several mechanisms of

diversity, such as space diversity, frequency diversity and time diversity. Furthermore the

ad-hoc nature of wireless networks allows for easy setup and re-configuration when the

network grows in size. Moreover where sensors and actuators are mounted on moving

parts, hard-wiring requires complex mechanical solutions that are costly and may limit

the freedom of movement of the part and present a potential cause of failure.

As sensors and actuators are part of closed-loop control systems, an industrial application

will require hard bounds on the maximum delay allowed during the communication, so

strict timing requirements apply. Another requirement is the coexistence of the network

with other equipment and competing wireless systems. The WirelessHART standard has

been released to fulfill all these demands.

4

2.3. WirelessHART Standard

2.3 WirelessHART Standard

WirelessHART is a wireless mesh network communication protocol for process automation

applications, including process measurement, control, and asset management applications.

It is based on the HART protocol, but it adds wireless capabilities to it enabling users

to gain the benefits of wireless technology while maintaining compatibility with existing

HART devices, tools and commands.

Each WirelessHART network includes three main elements:

• Field Devices that are connected to and characterize or control the Process or

Plant Equipment. All network devices, including field devices, must be capable of

routing packets on behalf of other devices.

• A Gateway which connects the WirelessHART network to a plant automation net-

work, allowing data to flow between the two networks. It enables communication

between Host Applications and field devices that are members of the WirelessHART

network. Every WirelessHART network includes one Gateway that, in turn, has one

or more network access points. They can be used to improve the effective through-

put and reliability of the network, as more packets per second through the network

are possible and the network is resistant to the failure of a single access point. It

is important to notice that a network access point is not directly connected to the

process, it is part of the Gateway.

• A Network Manager that is responsible for configuration of the network, schedul-

ing communication between network devices, management of the routing tables and

monitoring the health of the WirelessHART network. While redundant Network

Managers are supported by the standard, there must be only one active Network

Manager per WirelessHART network.

In the diagram in Figure 2.1 the WirelessHART network is connected to the plant automa-

tion network through a gateway. The plant automation network could be a TCP-based

network, a remote I/O system, or a bus such as PROFIBUS. The gateway is connected

to the WirelessHART network through network access points that increase the through-

put and improve the overall reliability of the network. All network devices such as field

devices and access points transmit and receive WirelessHART packets and perform the

basic functions necessary to support network formation and maintenance.

5


Figure 2.1: WirelessHART network components.

Devices can be deployed in a star topology, that is all devices are one hop to the gate-

way, to support a high performance application, a multi-hop mesh topology for a less

demanding application, or any topology in between. These possibilities give flexibility to

WirelessHART technology enabling various applications (both high and low performance)

to operate in the same network.

WirelessHART specifies the use of IEEE STD 802.15.4-2006 compatible transceivers op-

erating in the 2.4 GHz ISM (Industrial, Scientific, and Medical) radio band. The radios

employ DSSS (Direct Sequence Spread Spectrum) technology and channel hopping to

guarantee security and reliability. Communications among network devices are arbitrated

using TDMA (Time Division Multiple Access) that allows to schedule link activity.

2.3.1 TDMA Data Link Layer

WirelessHART uses TDMA and channel hopping to control access to the network and

to coordinate communications between network devices. The basic unity of measure is a

time slot which is a unit of fixed time duration commonly shared by all network devices

in a network. The duration of a time slot is sufficient to send or receive one packet per

channel and an accompanying acknowledgement, including guard-band times for network

wide-synchronization. The WirelessHART standard specifies that the duration of the time

slot is equal to 10 ms.

The TDMA Data Link Layer establishes links specifying the time slot and frequency where

6


communication between devices occurs. These links are organized into superframes that

periodically repeat to support cyclic and acyclic communication traffic.

All devices must support multiple superframes: different superframes may have lengths

that differ from each other and additional superframes can be enabled or disabled accord-

ing to bandwidth demand. Slot size and the superframe length (in terms of number of

time slots) are fixed and form a network cycle with a fixed repetition rate. However, a

superframe is fixed while it is active but its length can be modified when inactive.

Links may be dedicated or shared. Only two devices are assigned to a given dedicated slot,

one being the source and the other being the destination. A communication transaction

within the slot supports the transmission of a DLPDU (Data-Link Protocol Data Unit)

from the source followed right away by the transmission of an acknowledgment by the

addressed device.

Otherwise links may be shared between multiple sources, using contention-based access

as collisions may occur within a shared slots when more than one source try to convey

a packet within the same slot and channel. If a collision occurs, the destination device

will not be able to successfully receive any source’s transmission and will not produce

acknowledgement to any of them. To reduce the probability of repeated collisions, source

devices shall use random back-off delay when their transmission in a shared slot is not

acknowledged. Shared slots are allocated to provide base bandwidth and elastic bandwidth

utilization while minimizing power consumption.

For TDMA communications to be successful and efficient, all transactions have to occur in

slots following specific timing requirements thus synchronization of clocks between devices

in the network is critical. In particular, the network devices must have the same notion

of when each time slot begins and ends, with minimal variation.

Figure 2.2: Slot timing.

In this way, transmission of the source message can start at a specified time after the

7


beginning of the slot, allowing the source and the destination to set their frequency channel

and enabling the receiver to begin listening on the specified channel. It must start to listen

before the ideal transmission start time and continue listening after that ideal time due

to a tolerance on clocks. Once the transmission is complete, the communication direction

is reversed and the destination device indicates whether it received the source’s DLPDU

successfully or with a specific class of detected errors, by transmitting an acknowledgement

(see Figure 2.2).

To enhance reliability, channel hopping is combined with TDMA. It is a mechanism of

frequency diversity that allows to reduce interference from other sources and multi-path

fading effects. At the same time channel hopping provides channel diversity, that is each

slot may be used on multiple channels at the same time by different nodes.

NOTE: TIME KEEPING (DATA LINK LAYER)

2.3.2 WirelessHART Gateway

The WirelessHART Gateway is functionally divided into a Virtual Gateway providing a

sink or source point for the network traffic and one or more Access Points that provide the

physical connection into the WirelessHART network. If the gateway is made up of more

than one access point, the Network Manager will schedule communication traffic through

all of them.

The Gateway must provide the time synchronization messages to other network devices,

so the clock information ripples downward from the top of the network hierarchy to the

bottom, that is from the gateway to field devices. The virtual gateway communicates to

any field devices through network access points, so it must have a path to every device in

the network. On the other hand it can communicate directly with the Network Manager,

but this is an external connection. The network manager and the gateway must establish

a secure communication channel with each other, and maintain this connection to carry

control and data traffic.

The gateway can connect with the host application via various protocols (e.g. Modbus,

PROFIBUS) based on different physical layers. The network access points communicate

with the virtual gateway via a dedicated link or communication port. Moreover, each

access point can support communication with any device to which the network manager

has provided a path. As not utilizing every slot represents wasted opportunities, an access

point should have activity (e.g. transmit or receive) scheduled for every slot. Thus, if the

8


access points have nothing else to do they should advertise and perform shared listens.

It is important to notice that all communications with the WirelessHART network pass

through the gateway which must route packets to the specified destination (network device,

host application or network manager).

2.3.3 WirelessHART Network Manager

The Network Manager is central to the overall operation of the WirelessHART network.

It is responsible for the management, scheduling, monitoring, and optimization of com-

munication resources of the WirelessHART network. It manages both the WirelessHART

network and the network devices. To perform its complete set of functions it needs con-

figuration and setup information about the network devices that it reads from the devices

themselves, information about how the network is going to be used, and feedback from

the network about its overall health.

There is one network manager per WirelessHART network, and it may be co-located

with the Gateway in the same box or located in a completely separate physical box. It

is an application rather than a network device, so its location is not restricted by the

WirelessHART specification. However, the network manager must have a secure commu-

nication channel to the gateway.

The network manager forms the WirelessHART network and establishes routes, initializing

and maintaining network communication parameter values. It provides mechanism for

devices joining and leaving the network. It is also responsible for managing dedicated and

shared network resources, and for allocating communication resources. The allocation of

communication resources is referred to as scheduling.

The network manager establishes paths between the gateway and the network devices,

but after that it is not involved in communications between host applications and network

devices. The gateway is responsible for comparing the destination address of packets

with its own address and the network manager’s address. Whenever the gateway receives

packets destined for the network manager, it may remove the packet from the wireless

network and forward them to the network manager using its secure connection. Packets

with other destinations, as well as packets received from the network manager, are routed

into the network according to the routing described in the packet.

To generate the scheduling, the network manager combines information it has about the

topology of the network, heuristics about communication requirements, and requests for

9


communication resources from network devices and applications. In particular, in order

to schedule communication resources between network devices, the network manager must

know the update rate of each device.

As part of its system functions, the network manager collects network performance and

diagnostics about the behavior of the overall network. This information is available to be

reported to host-based applications but it is also used to adapt the network to changing

conditions. The adaptation includes updating route and schedule information, in order

to improve operation of the network while conserving power within devices. Reconfigura-

tion of the network may be performed while the network is operating as diagnostics are

accessible during run-time.

2.3.4 WirelessHART Network Schedule

A key characteristic of a WirelessHART network is the ability to automatically start up and

self-organize. However, before a WirelessHART network can form, a network manager and

a gateway must exist and they must have created a private connection with each other.

To initialize the network, the network manager must create the network management

superframe and the network graph that is an optimized route map.

NOTE: NETWORK MANAGEMENT SUPERFRAME

Management superframe has priority over data superframes and, following the Wire-

lessHART specifications, the network management superframe should be 6400 slots. When

the network manager creates the initial superframe, it assigns links in it for the gateway’s

access points and configures the gateway. It also assigns a dedicated superframe to the

gateway (the gateway superframe), in order to schedule activity of management of the

network which access points have to perform (such as listening of the channels to search

for new devices needing to join the network). Activating this first superframe the network

manager establishes the ASN (Absolute Slot Number, it indicates the actual time that is

being used for transmission of a specific packet) 0. The time when the network manager

starts the WirelessHART network is said to be the epoch for a specific network.

The network manager is also responsible to generate and to manage the network schedule.

In order to do so, it needs information about the network, the communication require-

ments, and the capabilities of the network devices. Using this information the network

manager is able to adjust the schedule to meet the requirements, and then to tune it by

using the feedback from the operation of the system.

10


The network manager allocates communication resources in terms of superframes and links.

A link is the full communication specification between adjacent devices in the network, that

is the communication parameters necessary to move a packet one hop. Each link includes

one time slot, a channel offset (for the frequency hopping), its type (transmit, receive

or shared), neighbor information, and transmit/receive attributes. Links are assigned to

superframes as part of the scheduling process.

The superframe is a set of slots repeating at a constant rate, these slots are called relative

slots, meaning that they are relative to the start of the superframe instead of being referred

to the epoch of the network. A frameID is assigned to each superframe. The superframe

size, i.e. the number of slots in the superframe, is the period of that superframe, that is

how often each slot repeats. In particular, the data superframe length is determined by

data scan rate.

Time slots are assigned to devices through links. For a dedicated link there will be a send

slot in one device and a receive slot in another device. If the link is shared then there will

be a receive slot in one device and one or more transmit slots in several devices, in other

words, shared links can have more than one talker and only one listener. When a device

has a shared link, it uses a collision-avoidance scheme with a backoff/retry mechanism to

handle collisions that may occur.

Using shared links may be suitable when throughput requirements of devices are low, or

when the traffic rate is irregular. In these situations, assigning shared links may decrease

latency because the network device does not need to wait for dedicated links, but this is

true only when chances of collisions are low.

The network manager creates a set of links for each device, it determines when the device’s

transceiver needs to wake up, and when it wakes up whether it should transmit or receive.

However the link does not determine what is communicated, it is only providing the

”opportunity” to communicate. A link assignment specifies how the network device shall

use a time slot. When a time slot is assigned to a device, the device can perform different

actions within that time slot, depending on the type of the associated link: it can attempt

to transmit a packet, wait to receive a packet, or remain idle.

All devices support multiple superframes of different sizes. All superframes logically start

in the same place in time: cycle 0, slot 0 of every superframe occurs at the beginning

of the epoch. Thus, time slots in different superframes are always aligned, even though

beginnings and ends of superframes may not be. Multiple superframes can be used to

11


define a different communication schedule for various sets of devices or to allow the en-

tire network to run at different communication rates. In fact, by configuring a network

device to participate in multiple overlapping superframes of different sizes, it is possible

to establish different communication schedules and connectivity matrices that all work at

the same time. But a network device with links in multiple superframes may encounter a

link arbitration situation. This may happen when two or more superframes with assigned

links coincide in the same absolute time slot. In these cases, the device must operate on

the link that has the numerically lowest frameID. For this reason, the gateway superframe

should be allocated with a large ID value. It is also required to be 40 slots long, this means

that the gateway superframe needs to be a minimum of 400 ms. Additional superframes

may also be allocated for event notifications or HART commands issued through host ap-

plications. It is important to notice that superframes can be added, removed, activated,

and deactivated by the network manager while the network is running.

2.3.5 Schedule Strategy

WirelessHART standard does not give any specification concerning about a particular

scheduling algorithm to be used in a WirelessHART network. However, there are some

references to be taken into account.

First of all, for all network devices accessed through the gateway, the user has to configure

how often each measurement value is to be communicated to the gateway. In order to

support multiple superframes for the transfer of process measurements at different rates,

the size of superframes should follow a harmonic chain in the sense that all periods should

divide into each other, in particular, scan rates should be configured as integer multiples

of the fastest update time that will be supported by network devices. For this reason, the

supported update rates will be defined as 2n where n is positive or negative integer values,

for example scan rate selections of 250 ms, 500 ms, 1 s, 2 s, 4 s, 8 s, 16 s, 32 s (or more).

The scheduling of communications associated with process measurements can be simplified

by defining a superframe for each scan period and developing the schedule by allocating

slots for transmission of measurement data starting with the fastest to the slowest scan

rate. To avoid any conflict between the slots reserved for process measurement and network

management, the length of the network management superframes may be configured to

be an integer multiple of the fastest scan rate and configured to use slots that are not

required for process measurement transmission. In this manner, slots may be allocated

12


for the transmission of measurement without any conflict with the slots dedicated for

management communications.

2.3.6 Communication Tables

Each network device (including field devices) contains tables controlling communication

activities and packet buffers, which are used to receive, process and forward packets. The

communication tables and the relationships between them are shown in Figure 2.3.

Figure 2.3: Communication tables.

The Superframe Table contains the identifier of the superframe, the number of slots in the

superframe, a flag indicating if the superframe is currently activated and a list of links.

The Link Table, in turn, contains a reference to a neighbor which is allowed to communicate

with the device, indicating the type of the link, the slot number in the superframe, the

frequency hopping channel offset and a flag indicating if the link may be used for receive

or for transmit.

The Neighbor Table has a primary importance in the management of device communi-

cations. It is a list of all neighbors of the device, which are all devices that the device

can directly exchange messages with. The neighbor table includes all the properties and

the statistics pertaining to the neighbor of the device, such as basic neighbor identity

information, performance and historical statistics and shared slots parameters.

The Graph Table maintains the identifier of the graph, optionally the destination address

and a reference to one or more neighbors. When a graph is used for routing, the list of

neighbors held by the graph table identify those devices that are legal destinations for the

packet’s next hop toward its final destination. For more details about graphs and routing

see the next subsection.

13


2.3.7 Graph Routing

WirelessHART networks can be configured in various topologies in order to support several

applications such as:

• star network, in which there is just one router device (the gateway) that communi-

cates with several field devices. It is suitable for a small application;

• mesh network, in which all network devices (including field devices) are router de-

vices. A mesh network is a robust network with redundant data paths which is able

to adapt to changing environments and more widespread applications.

• star-mesh network, that is a combination of the star network and the mesh one.

Figure 2.4: Network topologies.

In a star network all network devices are connected to the gateway through a single hop,

while mesh networks are multi-hop networks, that is, they use two or more wireless hops

to convey information from a source to a destination, thus requiring a routing algorithm

in order to allow the communication between network devices.

WirelessHART documentation does not specify a routing algorithm, it only describes

two methods of routing packets in a WirelessHART network: source routing and graph

routing. All devices must support both of them. Source routing specifies a single directed

route in terms of devices and links, between a source node and a destination node. The

source route is statically specified in the packet itself, that contains the list of devices

addresses composing the path toward the destination, thus intermediate devices require

no knowledge of the source route in advance. However if one of the intermediate link fails

the packet is lost, for this reason source routing should only be used for testing routes,

troubleshooting network paths or for ad-hoc communication.

On the contrary, in graph routing the graph route is a directed list of paths (subsets

of directed links and devices) that connect two devices within the network that need to

14


communicate, allowing to have redundant communication between network endpoints.

All intermediate devices must be pre-configured with graph information that specifies the

neighbors to which the packet may be forwarded.

The network manager is responsible to setup and manage all routes and to configure graph

information in each network device. In order to create efficient and optimized routes the

network manager needs information about the network, communication requirements and

the capabilities of network devices. Hence, when devices are initially added to the network,

the network manager stores all neighbors entries including signal strength information as

reported from each network device. Then it uses this information to build a complete

network graph which is an optimized route map, in the sense that possible but subopti-

mal links have been removed. In particular, the network graph is optimized in terms of

reliability, hop count, reporting rates, power usage and overall traffic load. As the over-

all network adapts to changing information, the network manager updates the topology,

adding or deleting information in each network device. The network manager contains

the network graph and portions of the graph that have been installed into each device.

Once the routing information and communication requirements for each device are known,

the scheduling of network resources can be performed for both scheduled upstream and

downstream communications.

Graphs are unidirectional, thus there are upstream paths which are used from field de-

vices to the gateway, for example for transferring process measurements and alarms, and

downstream routes that provide paths from the gateway to field devices, to send control

information, such as setpoint changes for actuators.

According to WirelessHART routing requirements, in a properly configured network, all

devices will have at least two devices in the graph through which they may send packets,

each graph should use a maximum of 4 neighbors as a potential next hop destination, the

minimum number of hops to be considered when constructing the graph is 2, the maximum

one is 4 and, if there is a one hop path to the gateway it should be used.

Every graph in the network is associated with a unique GraphID, a list of neighbors and

the destination’s address, but this one is an optional field since intermediate devices may

be merely forwarding the packet along the route path according to the GraphID. To send

a packet on a graph, the source device include a GraphID in the packet’s network header.

NEED TO INSERT NPDU STRUCTURE?

15


In addition to the communication tables mentioned above (see Subsection 2.3.6), in order

to be able to route packets along a graph, a device needs also to be configured with a

connection table containing entries that include the GraphID and neighbor address. The

device routing that packet must perform a lookup in the connection table by GraphID, and

send the packet to any of the neighbors associated with that packet’s GraphID. Once any

neighbor acknowledges receipt of the packet, the routing device may release it and remove

the packet from its transmit buffer. If an acknowledge is not received, the device will

attempt to retransmit the packet at its next available opportunity. This means that, when

a field device does not communicate directly with the gateway, then added communication

slots must be reserved in the schedule for packet routing.

16

Chapter 3

Scheduling Theory

3.1 Introduction

A real-time system is a system in which the correctness of the system behavior depends

not only on the logical results of the computations, but also on the physical instant at

which these results are produced, in other words, it is a system with explicit deterministic

(or probabilistic) timing requirements. Real time systems can be viewed as an important

subclass of embedded systems. These are most often subject to limited computation

resources as a result of economic considerations. This combined with the trend of having

more functionality being realized in software, make resource scheduling and its effect on

control performance a relevant issue. A key issue in real-time systems is predictability, i.e.

to be able to anticipate the behavior of the system before run-time, and the guarantee that

the system will behave as anticipated at run-time. At the same time, run-time flexibility

is a desired feature, as not all run-time events can be completely taken into account in

advance. In addition, the choice of scheduling strategy in real-time systems is strongly

related to the nature of the timing constraints which have to be fulfilled. As different

scheduling schemes provide different levels of, for example, predictability or flexibility,

there is usually a trade-off between the ability to handle complex constraints and the level

of flexibility provided by the selected scheduling strategy.


17

3.2. Scheduling Algorithms

3.2 Scheduling Algorithms

Real-time scheduling theory offers a way of predicting the timing behavior of complex

multi-tasking computer software, assuming that a real-time system consists of the following

components:

• a set of computational and communication tasks {τ1, τ2, ..., τn} to be performed

fulfilling a number of timing requirements, where a task τi is a sequence of jobs or

operations {Ji,1, Ji,2, ..., Ji,ki} for i = 1, ..., n;

• a run-time scheduler (or dispatcher) that controls which task is executing at any

given moment;

• a set of resources shared by the set of tasks. All communication and synchronization

between tasks are assumed to occur via shared resources.

Each job has a release time and a computation time. The release time is the time in

which the job becomes available for processing that requires a computation time c. For

a task the deadline is the time interval within which all task’s jobs must finish executing

and it is specified relative to the arrival time of the task invocation, thus defining also

the corresponding absolute deadline. The purpose of the deadline is to constrain the

acceptable finishing time for a task. The task characteristics can be specified by a set β

made up of 4 elements {β1, β2, β3, β4}. β1 describes precedence relations between jobs,

that are represented by means of an acyclic directed graph G = (V,E) where V is the

set of jobs and, ∀i, j = 1, ..., n, ∀x = 1, ..., ki, ∀y = 1, ..., kj , (ix, jy) ∈ E iff Ji,x must be

completed before Jj,y starts (notice that the involved jobs can belong to the same task or

to different tasks). INSERT FIGURE AND REFERENCES

If there are dependencies between jobs β1 = prec. If β2 = ri, then release dates may be

specified for each task. If ri = 0 for all tasks, then β2 does not appear in β. β3 specifies

restrictions on the computation time for each job of a task. If β3 is equal to ci,ki= 1 then

each job has a unit processing requirement. If β4 = di,kithen a deadline is specified for

the job Ji,kibelonging to the task τi. This means that the job Ji,ki

must finish within the

time interval di. Given an arbitrary set of tasks, the corresponding scheduling problem

is to find a schedule of these tasks satisfying certain restrictions and optimizing one or

more performance measures. A schedule is said to be feasible if the temporal constraints

of tasks are met at run-time (e.g. if all tasks are executed within a certain time interval).

18


A task is periodic if it is time-driven, with a regular release (invocations are all identical

and arrive at fixed time instants). The time interval between two successive invocations of

the task τi is a constant Ti, and it is called the period of the task. If the relative deadline

for a periodic task is left unstated it is usually assumed to be equal to the task’s period.

A task is aperiodic if it is not periodic, it consists of a sequence of invocations which

arrive randomly, usually in response to some external triggering event, thus it is event-

driven. Sporadic tasks are a special case of aperiodic ones but they have a fixed minimum

inter-arrival time. Task invocations may be further categorized by their availability to be

preempted while executing. Following these classifications, tasks can be non-preemptive,

if task invocations execute to completion without interruption once started, or they can

be preemptive if task invocations can be temporarily preempted during their execution by

the arrival of a higher-priority invocation. In scheduling theory the notion of priority is

commonly used to order access to shared resources such as processors or communication

channels. Scheduling algorithms can also be divided in two big classes: off-line or static

algorithms and on-line or dynamic algorithms.

3.2.1 On-line scheduling

On-line scheduling algorithms are suitable for event-triggered systems as they provide the

capability to handle dynamic on-line events. They require a complex scheduler that has

to make decisions about which task to execute at run time, based on the priorities of

the task invocations. As it is a priority-based approach, this scheduling policy can be

further categorized with respect to the priority, thus determining fixed-priority algorithms

and dynamic-priority algorithms. In fixed-priority algorithms the dispatcher statically

associates a priority with each task in advance.

Two basic priority assignment rules are the Rate Monotonic (RM) algorithm and the

Deadline Monotonic (DM) scheduling. According to RM, tasks are assigned fixed priorities

ordered as the rates, so the task with the smallest period receives the highest priority.

Instead, in DM tasks with shorter deadlines are allocated higher priorities. In dynamic-

priority scheduling the priority of each task is determined at run-time. Typically this

requires a more complex run-time scheduler than fixed-priority scheduling. One of the most

used algorithms belonging to this class is the Earliest Deadline First (EDF) algorithm,

according to which task priorities are inversely proportional to the absolute deadlines.

Considering a physical plant interacting with a controller that measures some plant signals

19


and generates appropriate control signals in order to influence the behavior of the plant,

another approach is to combine scheduling theory and control theory to achieve higher

resource utilization and better control performance. In this case, the on-line scheduler

uses feedback to dynamically adjust the control task attributes in order to optimize the

global control performance, trying to keep the resource utilization at a high level and

distributing the computing resources among the control tasks. In particular, in feedback-

feedforward scheduling of control tasks, the dispatcher uses feedback from execution time

measurements and feedforward from workload changes to adjust the sampling period of

the controller, so that the performance of the closed-loop control system is maximized.

3.2.2 Off-line scheduling

Off-line scheduling algorithms require the programmer to define the entire scheduling prior

to the execution. According to this table-driven approach the time line is divided into

slots of fixed length (minor cycle) and tasks are statically allocated in each slot based on

their rates and execution requirements. The schedule is then constructed up to the least

common multiple of all periods (called the hyperperiod or the major cycle) and stored in

a table. At run-time, tasks are dispatched according to the table and synchronized by a

timer at the beginning of each minor cycle. Given a set of periodic processes the problem

to schedule them meeting their deadline and period constraints is known to be NP-hard

for one processor, which means that in the worst case an exponential amount of work

appears necessary to determine whether a feasible solution exists. In other words, in the

worst case an exhaustive search is necessary in order to determine if a schedulable solution

exists or not.

In practice, most cyclic executive schedules are derived manually, but they can also be

automated, despite deriving an optimal schedule is theoretically an intractable (NP-hard)

problem. On one hand, off-line algorithms allow to consider and to manage complex

dependencies between tasks and resource contention among jobs when constructing the

static table. Moreover, this policy produce programs that are entirely deterministic, so it

is possible to know which task is executing at any given time. As tasks always execute

in their preallocated slots, the experienced jitter is very small. Furthermore, the entire

schedule is captured in a static table, so different operating modes can be represented

by different tables. On the other hand, this scheduling policy is fragile during overload

situations, since a task exceeding its predicted execution time could generate a domino

20


effect on the subsequent tasks, causing their execution to exceed the minor cycle boundary.

In addition, off-line scheduling is not flexible enough for handling dynamic situations. In

fact, a creation of a new task, or a change in a task rate, might modify the values of the

minor and major cycle, thus requiring a complete redesign of the scheduling table. So, the

off-line scheduling approach is more suitable for static configuration of systems. Another

potential source of inefficiency is the need to fit all activities into common multiples of the

major and minor cycle (so if an activity does not fit exactly into the schedule it may be

necessary to rewrite the code to make it fit).

21

Chapter 4

Automation System 800xA

4.1 Introduction

The Industrial IT System 800xA is a process automation system that extends the scope of

traditional control systems incorporating all automation functions in a single environment,

and embracing the principles of real time computing and networking. The 800xA system

functionality is divided into a Base System, which is the system base software and a set

of options or functions that can be added to the system based on the needs of the process

that should be controlled. Controllers are integrated with the system through integration

functions. Generally, the 800xA system is used together with the AC800M controller.


4.2 AC800M Controller

Due to its modularity the AC800M controller can be used for a wide range of applications.

It supports industry standard fieldbuses and communication protocols such as Ethernet,

PROFIBUS, FOUNDATION Fieldbus and HART, via embedded or external communi-

cation interfaces. The AC800M controller supports the S800 I/O, a distributed modular

I/O system for communication via PROFIBUS or directly connected to the controller.

INSERT REFERENCE DOC 3BSE038018R5011

In Figure 4.1 the 800xA system network architecture is shown. The automation system

network is a real time LAN (Local Area Network) optimized for high performance, and

it is used for communication between workplaces, servers and controllers. Workplaces

provide various forms of user interaction, whereas servers run software that provides system

22

4.2. AC800M Controller

Figure 4.1: 800xA system network architecture.

functions. In a cabled network fieldbuses are used to interconnect field devices such as

sensors, actuators, and I/O modules, and to connect these devices to the system, either

via a controller or directly to a server.

As an alternative a WirelessHART network can be used to interconnect field devices and

to connect them to a gateway, which is in turn connected to the controller by means of

fieldbuses. The automation system network can be connected to a plant network, such as

an office or corporate network, via some form of secure network interconnection such as

an isolation device. The nature of the secure interconnection depends on the nature of the

plant network and the level of security that is required for the considered application.

The automation system network can also be split into a client/server network and a control

network for larger systems or for systems where network separation is required e.g. for

system integrity reasons. The control network is based on TCP/IP over Ethernet, so it

is a private IP network domain especially designed for industrial applications, and where

addresses are static and must be selected according to a scheme defined by the RNRP

(Redundant Network Routing Protocol). It is a routing protocol developed by ABB which

supports redundant network configurations as it handles alternative paths between nodes

and automatically adapts to topology changes. A redundant network consists of two

fully separate Ethernet networks. Each node has two IP addresses, one on the primary

network, and one on the backup network. Detection of a network failure and a switch over

23

4.3. Evolution of Control Technology

to the redundant network takes less than one second, with no loss or duplication of data.

Following the RNRP protocol, each node cyclically sends a routing vector as a multi-cast

message on both networks. The routing vector indicates which other nodes this node can

see on the network. Each node uses received routing vectors to build a table, listing which

nodes can be reached on which of the two networks. One of the networks is designated

as the primary network, the other one as the backup network. As long as the primary

network works, all traffic is sent on that network , only routing vectors are sent on the

backup network to verify that it works.

The control network can be a very small network with a few nodes or a large network

containing a high number of network areas. Normally, the control network covers one

manufacturing plant. A large control network can be divided into subnetworks (network

areas), for example to keep most of the time-critical communication within smaller areas,

thereby improving performance. Network areas are interconnected by means of RNRP

routers. The AC800M controller also has router capability, but since it only has two

network ports it can only be a router between two non-redundant network areas. It is not

possible to do routing between two redundant network areas by using two AC800M with

one on each path. The number of nodes in one control network segment is limited, due

to limited routing resources in controller nodes, and to the load generated from RNRP in

the controllers.

It is important to notice that communication performance is affected by bandwidth, mes-

sage length and cyclic load. In particular, the actual communication throughput for a

controller mainly depends on the cycle time of the applications and the CPU load in the

controller, rather than the ethernet speed.

4.3 Evolution of Control Technology

The evolution of controls technology can be resumed in the three phases: classical control

(in which the process control systems were analog and made up of simple devices with

signal formats that were essentially determined by the need for an architecture with a

minimum number of costly CPUs), digital control, and networked control.

The milestones of this development were the introduction of negative feedback amplifiers,

field adjustable PID controllers, and especially digital computers. These technologies have

had a huge impact on control theory and its application, and today they are strictly linked

24


with the PC hardware revolution.

As regard networked control, control systems with spatially distributed components have

existed for several decades. However, in the past, in such systems the components were

connected via hardwired connections and the systems were designed to bring all the in-

formation from the sensors to a central control station. The control policies then were

implemented via the actuators, such as valves or motors. Moreover, before digital com-

munications was introduced, sensors and actuators were hardwired to their controllers

and it was impossible to transmit more than a single process or manipulated variable. In

addition, analog signals only traveled in one direction, from the transmitting sensor to the

controller or from the controller to the receiving actuator.

As the networking used in automation is predominantly digital, the trend is toward all-

digital communications, that makes it possible to extract much more information from

each device than was possible using analog signals. The advent of digital communica-

tions makes it possible for controllers to be placed away from sensors and actuators, as

all information for hundreds of loops and monitoring points could be transmitted to the

controller over a single network. Digital communications carry not only I/O like process

and manipulated variables but also operational information such as setpoint, alarms, and

tuning in both directions to and from the controller. Thus communication enables dis-

tributed processing, and some functions can be added not only in controllers but also in

field instruments. In this way, thanks to communications, field devices perform not only a

basic measurement or actuation but also have features and functions for control and asset

management. Moreover, using a digital signal for control there is no analog conversion

from the device to the signal wires, and then from the signal wires to the host, which

increases the accuracy of the process variable.

A second big benefit of digital communications is the capacity to connect several devices

to the same single pair of wires to form a multidrop network that shares a common

communication media. Compared to running a separate wire for each device (like in

analog communication), this reduces the wiring requirement, especially for plants with

large distances and many devices. Hence, the amount of required cables is greatly reduced,

allowing hardware and installations savings.

In industrial environment, the set of digital communication protocols used for distributed

control is represented by Fieldbus systems (see Section 4.5 for further information). In

the simplest form of communication, a device such as a host workstation or a controller

25


is the master that sends requests to read or write a value to other devices such as field

instruments, which are called slaves. The slave that was addressed then respond to the

request. An example of this master/ slave communication is the HART or the PROFIBUS

architecture. In a network with no specific master or slaves such as FOUNDATION

Fieldbus the client/server method is used. In this case a device acting as a client requests,

and the device acting as a server responds. Another mode of communication is when a

device acting as a source transmits a message to a device acting as a sink without any

request from the sink. The choice of the communication protocol depends on the particular

application as buses are optimized for different characteristics. The common characteristic

of Fieldbuses is that they allow many devices to be connected on the same wire and provide

the necessary addressing mechanism to support communication with them.

Several Fieldbus manufacturers have recognized also the advantages of Ethernet, which is

another standard bus system for industrial applications. These advantages are related to

the physical layer, particularly in terms of bandwidth, which can be higher than 100Mb/s

rather than up to 12Mb/s for fieldbuses. However both of them can be considered high

speed protocol when compared with typical requirements of most industrial applications,

where the required data throughput of the network is relatively low, but its reliability needs

to be very high. The disadvantage of fieldbus are its higher installation and purchase

costs for fieldbus devices. Actuators and sensors are often relatively inexpensive when

compared with the cost of the cable used to connect them. Beyond the high installation and

maintenance costs, the high failure rate of connectors and the difficulty of troubleshooting

them have to be considered.

These are the main reasons why in industrial environments, apart from lower installation

and maintenance costs, wireless systems can offer ease of equipment upgrading and prac-

tical deployment of mobile robotic systems (see Section 2.2). It must be noted that the

common trend in the semiconductor industry has been towards ever more complex inte-

grated circuits with a higher transistors number (thanks to the continuing success of the

Moore’s law) and higher clock frequencies. This trend, in combination with recent tech-

nological advances in MEMS (Micro Electro Mechanical Systems), enables an increasing

integration of devices at a lower cost. However, integrating devices through wireless rather

than wired communication channels has highlighted important potential application ad-

vantages but, at the same time, also several challenging problems for current research. In

fact, one of the main characteristics of wireless networked control is that complexity of

26

4.4. Clock synchronization

the application lies not in the individual nodes, but in the collaborative effort of a large

number of distributed elements. This means that, even if high clock frequencies technol-

ogy and wireless data rates of many Mbit/s are available, individual nodes use a restricted

amount of computational power and wireless communication protocol with data rates of

the order of tens Kbit/s. This observation is quite crucial, as in some sense goes against

the dominant trends in both the world of computation and wireless communication, where

the aim is to reach high computational power and high data rates. The first reason of this

characteristic of networked control is due to the requirements of most applications, that is

regular and low frequency transmission of small data packets. Moreover, the distributed

nature of the embedded wireless network allows to split the computational power over a

collection of wireless devices, rather than relying on a central communication coordina-

tor, or at least to perform some functions on the field devices themselves. However, the

main reason to use a restricted amount of the possible computation power and low data

rates is due to the nature of the wireless communication between devices. In particular,

performance of a wireless network has to be optimized with respect to constraints on com-

munication bandwidth, contention of communication resources, delay, jitter, noise, fading,

and energy usage. Energy conservation is a key requirement in the design of wireless

networks. This is mainly due to the limited power availability. In fact, as the speed of

embedded processors increases and more peripherals are integrated into a single chip, the

applications that run on these devices become more computationally intensive. However,

technological advantages of the batteries which power the embedded systems lags signifi-

cantly behind, and as a result, power consumption is one of the most important issues for

wireless embedded systems. In order to reduce the energy spent in communication, the

wireless network should use bounded data rates, as the power spent in communication is

the principle source of energy consumption. A good power management technique reduces

also long term fading effects and interference. As a consequence of this power management

policy devices will transmit with the minimum transmit power level both to save energy

and to reduce fading and interference on the shared wireless medium.

4.4 Clock synchronization

Typically a controller will act as time master for the control network and the rest of the

system will be synchronized from this source. The time source can be a controller or one

27

4.5. Fieldbus Standards

external time source. It is advisable to use an external time source if it is important that

time stamps in the system are possible to compare with time stamps from other systems.

The AC800M supports clock synchronization by four different protocols: CNCP, SNTP,

MB300 Clock Sync, and MMS Time Service. The AC800M can send clock synchronization

with all protocols simultaneously, but it will itself only use the one configured protocol to

receive clock synchronization from another source. One usage of this is to let it receives

tim with one protocol and distribute to other nodes with another protocol.

CNCP (Control Network Clock Protocol) is an ABB proprietary master-slave clock syn-

chronization protocol for the control network. When using the AC800M controllers CNCP

is the recommended protocol for time synchronization to all nodes on the control network

that support CNCP.

SNTP (Simple Network Time Protocol) is a standard clien/server oriented time synchro-

nization protocol. If the control system needs to be synchronized with a global time

master, the recommended method is to use an SNTP server with a GPS receiver. The

same holds if the system is made up of more than one control network.

The AC 800M OPC Server supports the MMS Time Service for small systems where no

AC 800M is used for backward compatibility with older products. MB 300 Clock Sync

is a protocol for clock synchronization of Advant/Master products on a MasterBus 300

network.

4.5 Fieldbus Standards

Fieldbus systems are used as means of communications for serial data exchange between de-

centralized devices on the field level and the controller of the process supervision level. All

relevant signals such as input and output data, parameters, diagnostic information, con-

figuration settings and, for a wide range of applications, the power required for operation

can be carried over two wires. Obviously, if a field device has a high power requirement,

then this device can be powered externally. Historically, communication between field

devices and a control system has been over analog 4-20 mA current loop interfaces. This

widely used technology works well and allows accurate transmission of process variable

measurements as well as effective closed-loop control.

Fieldbus technology, however, is digital. This brings remarkable simplification to system

architectures and, principally, when digital field instruments are used, significantly more

28

4.5. Fieldbus Standards

data becomes available. Technically, Fieldbus is a fully digital and duplex data transmis-

sion system, which connects smart field devices and automation systems to an industrial

plant’s network. A Fieldbus differs from point-to-point connections, which allow only

two participating devices to exchange data. In fact, the key attribute to Fieldbus com-

munications is higher speed communications with the possibility of addressing multiple

transmitters all on the same field wiring. Since the Fieldbus concept is a computer network

specifically designed to support realtime, field sensor/actuator level messaging, most of

the Fieldbus systems available today adopt the ISO/OSI 7 layer model, but in a reduced

stack architecture.

There are hundreds of different Fieldbus protocols, but not all of them are recognized as

standards. Currently, PROFIBUS and FOUNDATION Fieldbus are the accepted Fieldbus

standards for the automation industry. Both of them can provide pure digital communi-

cations between field devices and the control systems. In terms of Fieldbus organization

they are similar with many user groups and support worldwide by the major manufac-

turers, whereas their technologies are different in several important areas. ABB supports

both PROFIBUS, and FOUNDATION Fieldbus, that are standards of the IEC (Inter-

national Electrotechnical Commission). PROFIBUS and FOUNDATION Fieldbus are

Fieldbus standards for applications in the manufacturing industry, process automation

and building automation.

The PROFIBUS family mainly consists of PROFIBUS-DP and PROFIBUS-PA. The first

one is optimized for high speed and simple connection of devices, and it is especially

designed for communication between programmable controllers and a distributed I/O level.

PROFIBUS-PA is especially designed for process automation, and it allows sensors and

actuators to be connected to the same bus, even in security areas. The transfer rate varies

from 9.6Kbit/s to 12Mbit/s.

The FOUNDATION Fieldbus is dedicated to the establishment of a single, open, inter-

operable Fieldbus. The standard developed by this Foundation, Foundation Fieldbus,

might emerge as one of the world’s dominant Fieldbus protocols for process control in the

very near future as this technology seamlessly integrates with Ethernet technology offering

high speed and reliable automation solutions for the process industry at affordable costs.

FOUNDATION Fieldbus defines two communication profiles, H1 and HSE. The H1 profile

has a data transmission rate of 31.25 Kbit/s and it is mostly used for direct communica-

tion between field devices in one link (H1 link). The HSE profile with a transmission rate

29

4.6. S800 I/O

of 100 Mbit/s serves first and foremost as a powerful backbone for the link between H1

segments.

Also the Modbus protocol is supported by ABB, which is a messaging structure to establish

master-slave/client-server communication between intelligent devices. It is an open stan-

dard network protocol widely used in the industrial manufacturing environment. Modbus

is executed serially and asynchronously according to the master/slave principle, and in

one direction at a time (half-duplex). It is used mainly for reading and writing variables

between control network devices, using point to point or multidrop communication.

ABB supports also HART devices although HART is strictly spoken not a fieldbus. The

HART communication protocol is an open protocol from the HART Communication Foun-

dation (HCF), and it has been developed in the late 1980’s to facilitate communication with

smart field devices. HART (Highway Addressable Remote Transducer) allows simultane-

ous analog and digital communication. The analog signal carries the process information,

while the digital one allows bi-directional communication and makes it possible for addi-

tional information beyond just the normal process variable to be communicated to/from

a smart field instrument. It can be considered a hybrid bus designed for configuration,

maintenance and other devices functions while maintaining compatibility with the huge

installed base of analog only hosts. HART is a master/slave protocol which means that

a field (slave) device only speaks when spoken to by a master. The HART protocol can

be used in various modes for communicating information to/from smart field instruments

and central control or monitoring systems. In 2007 the HART Communication Foundation

(HCF) has released the HART 7 Specification. Included in HART 7 is WirelessHART,

an open wireless communication standard especially designed specifically to address the

needs of the process industry for simple, reliable and secure wireless communication in

real world industrial plant applications (see Chapter 2 for a more detailed description).

WirelessHART supports both new wireless field devices and also retrofit of existing HART

devices with WirelessHART adapters.

4.6 S800 I/O

The S800 I/O is a distributed and modularized I/O system. It is a predefined device in

AC800M both as direct I/O using the built in AC800M ModuleBus connections, and as

remote I/O via PROFIBUS.

30

4.6. S800 I/O

It communicates via PROFIBUS or Fieldbus and contains digital and analog input/output

modules. The high modularized I/O station is made up of

• input and output modules, covering analog and digital signals of various types, and

interfaces for RTDs (Resistance Temperature Detectors) and TCs (Thermocouple)

of various types;

• power supply;

• Fieldbus Communication Interface (FCI);

• Fieldbus;

• ModuleBus.

Figure 4.2: S800 I/O station overview.

The S800 I/O uses PROFIBUS as an external communication interface (the S800 I/O

station works as a slave on PROFIBUS) and ModuleBus as an internal communication

link between clusters and their I/O modules. The S800 I/O provides also possibilities to

communicate with HART field devices via PROFIBUS. There are 4 I/O modules that

have HART interface. In total, the S800 I/O station can have up to 24 I/O modules.

Modules can be grouped into clusters, with an FCI for each cluster. The FCI connects

S800 I/O modules to a controller by way of different types of fieldbuses. The FCI can

31

4.6. S800 I/O

be used also to connect the S800 I/O station to a PROFIBUS network. The Fieldbus

Communication Interface is a configurable communication interface and it is responsible

for

• module configuration and supervision,

• performing signal conditioning on input and output values,

• dynamic transfer of information.

The FCI performs the signal conditioning for the more basic I/O modules. This means

that it has to make come computation before moving the value to the module or after

reading the value from the module. The type of signal conditioning to perform depends

on the module type and its configuration (parameter settings). Intelligent I/O modules

do signal conditioning themselves. In this case the FCI only has to move the value to or

from the module. Thus, in this case there is less load on the FCI which can be used on

other modules or services. Figure 4.3 gives an overview of how the exchange of dynamic

process data is transferred back and forward between the user application and the actual

process. The transportation of dynamic data between PROFIBUS and the ModuleBus

Figure 4.3: S800 I/O Dynamic Data Exchange.

is the main task for the FCI. The Fieldbus Communication Interface has a dedicated

32

4.6. S800 I/O

memory area when it sends the output values and reads the input values. The CPU in

the FCI performs the rest of the data transportation. It reads output values from the

memory and writes to the I/O modules via the ModuleBus and vice versa. The data

transfer between PROFIBUS and the ModuleBus is not synchronized. Read and write

operations are performed from and to a dual port memory in the FCI. The FCI scans the

I/O modules cyclically, with a cycle time depending on type and number of modules (from

4 to 108 ms)

4.6.1 S800 I/O Station Data Scanning

The ModuleBus data is scanned (read or written) cyclically, depending on the I/O module

configuration. To calculate the I/O scan cycle time in the FCI the following expression

can be used: ∑i

ni × ti (4.1)

where ni is the number of modules of type i, and ti is the execution time for modules of

type i. If the value is a multiple of 2, add 2 to the value. Otherwise increase the total

value to the nearest higher multiple of 2 to get the I/O scan cycle time. Digital input and

digital output modules will be scanned each I/O scan cycle time. Instead, on one scan 1/4

of the analog modules and 1/10 of the slow analog modules are scanned. Thus, it takes

four scans to read all analog modules, ten scans to real all slow analog modules, and one

scan to read all digital modules.

Example S800 I/O Station Data Scanning

Suppose to consider a S800 I/O station with the Fieldbus Communication Interface CI801

(t = 1.18ms), two analog input modules AI810 (t = 3.00ms), one analog output module

AO810/AO810V2 (t = 1.20ms), two digital input modules DI810 (t = 0.43ms), two digital

output modules DO810 (t = 0.43ms), and one analog input module AI830/AI830A (t =

33

4.6. S800 I/O

0.40ms). The I/O scan cycle time can be obtained as in the following.

2 AI810 ⇒2 ∗ 3.00ms = 6.00ms

1 AO810/AO810V2 ⇒1 ∗ 1.20ms = 1.20ms

2 DI810 ⇒2 ∗ 0.43ms = 0.86ms

2 DO810 ⇒2 ∗ 0.43ms = 0.86ms

1 AI830/AI830A ⇒1 ∗ 0.40ms = 0.40ms

1 CI840 ⇒1 ∗ 1.18ms = 1.18ms

10.50ms

10.50 is not a multiple of 2, thus the increase of the value to the nearest multiple of 2

gives 12 ms. This will give an I/O scan cycle time of 12 ms between the FCI and its I/O

modules. This means that the digital modules will be scanned every 12 ms, the AI810

and the AO810/AO810V2 every (4*12 ms) 48 ms, and the AI830/AI830A every (10*12

ms) 120 ms.

34

Chapter 5

Process Control

5.1 Introduction

Process control systems are normally complex with many control variables and many

measured signals. There are two general approaches for designing a complex system,

bottom up and top down. In the bottom-up approach the system is designed by combining

simple components and small subsystems, whereas the top-down approach starts with a

general overall design that is refined successfully. This chapter will deal with components

required to build complex automation systems using the bottom up approach. The key

component is the PID controller and it will be described in Section 5.2. Other important

control principles such as cascade control, mid-range control, split-range control and ratio

control will be discussed, respectively, in Sections 5.3, 5.4, 5.5, 5.6.

5.2 PID Control

The PID controller is one of the most common control algorithms, widely used in industrial

control systems. The general empirical observation is that most industrial processes can be

controlled reasonably well with PID control provided that the demands on the performance

of the control are not too high, in that case more sophisticated control is advisable. It is a

feedback system and it is able to solve a wide range of control problems. The PID controller

is used also in hierarchical systems, such as model predictive control, at the lowest level;

the multivariable controller gives the set points to the controllers at the lower level. In

this section a basic PID algorithm will be described, although several modifications of

this basic control structure must be implemented in order to improve performance and

35

5.2. PID Control

operability, and to obtain a practical, useful controller. Consider the simple feedback loop

illustrated by the block diagram in figure , where u is the control signal, that influences the

process by means of an actuator. The process output is denoted by y and it is measured

by a sensor. Both the actuator and the sensor are considered part of the process. The set

point (or reference value) is the desired value of the process output and it is denoted by

ysp. The control error e is the difference between the set point and the process output,

i.e. e = ysp − y. The control law of the PID controller can be expressed as:

u(t) = K

(e(t) +

1Ti

∫ t

0e(τ)dτ + Td

de(t)dt

)(5.1)

where K is the proportional gain, Ti is the integral time, and Td is the derivative time.

The control action is a sum of three terms: the P-term (Proportional to the error), the

I-term (proportional to the Integral of the error), and the D-term (proportional to the

Derivative of the error). These three terms will be described in the following subsections.

5.2.1 Proportional Action

In the proportional control the characteristic of the controller is proportional to the control

error for small errors:

u(t) = Ke(t) + ub (5.2)

where ub is a bias, which sometimes can be adjusted manually and reset to zero. In order

to better understand some properties of the proportional controller consider the static

model of the simple feedback loop shown in figure ADD FIGURE + REFERENCE to FIGURE

3.1 pag 65 :

x = Kp(u+ d)

y = x+ n

u = K(ysp − y) + ub

(5.3)

where x is the process variable, d is the load disturbance, n is the measurement noise,

Kp is the static process gain, and K is the proportional gain. Eliminating intermediate

variables the relation between the process variable x, set point ysp, load disturbance d,

and measurement noise n can be described as:

x =KKp

1 +KKp(ysp − n) +

Kp

1 +KKp(ub + d) (5.4)

36

5.2. PID Control

where KKp is called loop gain (it is a dimensionless number). From Equation 5.4 several

properties of the closed-loop system can be described. Suppose that n = 0 and ub = 0,

then Equation 5.4 reduces to:

x =KKp

1 +KKpysp +

Kp

1 +KKpd (5.5)

In this case, a high value of the loop gain KKp ensures that the process output x is close

to the set point ysp. If the controller gain K is high, then the system is also insensitive to

the load disturbance d. However, if n 6= 0 the measurement noise influences the process

output x in the same way as the set point ysp, thus the loop gain should not be made

too large. Moreover, if ub 6= 0 the controller bias influences the system in the same way

as the load disturbance d. This means that the design of the loop gain is a trade-off

between different requirements and control objectives. Further, for a dynamic system

the maximum value of the loop gain is determined by the process dynamics, because in

this situation the closed-loop system will be normally unstable for high loop gains if the

process dynamics are considered. It also follows from Equation 5.4 that there will be a

steady-state error with proportional control, but from Equation 5.3 it can be seen that

the control error is equal to zero only when u = ub in stationarity. Hence, with a proper

choice of the controller bias ub, the error can be made zero at a given operating condition.

5.2.2 Integral Action

Proportional control has the drawback that the process variable often deviates from the

set point in steady state. This can be avoided by making the control action proportional

to the integral of the error. With integral action, a small positive error will always lead

to an increasing control signal, and a small negative error will give a decreasing control

signal. In order to show that the steady-state error will always be zero with integral action,

assume that the system is in steady state with a constant control signal u0 and a constant

error e0. It follows from Equation 5.1 that

u0 = K

(e0 +

e0Tit

)(5.6)

Since u0 is a constant it follows that e0 must be zero. Integral action can also be visu-

alized as a device that automatically resets the bias term ub of a proportional controller.

Considering the PI controller:

u(t) = K

(e(t) +

1Ti

∫ t

0e(τ)dτ

)(5.7)

37

5.3. Cascade Control

the case Ti = ∞ corresponds to pure proportional control, whereas if Ti has finite values

the steady-state error is removed. Furthermore, for large values of the integration time, the

response creep slowly towards the set point, with an approach approximately exponential.

For smaller values of Ti the approach is faster but is also more oscillatory.

5.2.3 Derivative Action

The purpose of the derivative action is to improve the closed-loop stability. In a controller

with proportional and derivative action the control signal is made proportional to an es-

timate of the control error at Td time units in the future, where the estimate is obtained

by linear extrapolation.

Thus, in a PID controller the control signal is a sum of three terms:

• the I-term, representing the past by the integral of the error; it ensures that the

steady state error becomes zero;

• the P-term, representing the present;

• the D-term, representing the future by the time derivative of the control error; it

allows prediction of the future error.

It could be shown that PI control is adequate for all processes where the dynamics are

essentially of the first order, thus many industrial controllers only have PI action and

derivative action is frequently not used. For processes where the dominant dynamics are

of the second order PID control is sufficient, there are no benefits gained by using a more

complex controller. However, when the system is of an order higher than two, the control

can be improved by using a more complex controller than the PID controller.

5.3 Cascade Control

Cascade control can be used when there are several measurement signals but only one

control variable, to enhance control performance through the use of extra measurements.

Cascade control can be built up by nesting two control loops, as shown in Figure ADD

FIGURE + REFERENCE 12.4 PAG 373 , where an intermediate measured signal responding

faster to the control signal is used. The inner loop is called secondary loop, whereas the

outer loop is called the primary loop as it deals with the primary measured signal. It

38

5.4. Mid-Range Control

is also possible to have a cascade control with more nested loops. The performance of a

system can be improved with the number of measured signals, up to a certain limit. The

key idea of cascade control is to arrange a tight feedback loop around a disturbance. As a

rule, the secondary loop is used to eliminate fast disturbances, whereas slow disturbances

can be eliminated by the primary loop. Thus it is important to choose in a proper way

the secondary measured variable. The basic rules for selecting it are the following:

• there should be a well-defined relation between the primary and secondary measured

variables;

• essential disturbances should act in the inner loop;

• the inner loop should be faster than the outer loop;

• it should be possible to have a high gain in the inner loop.

A common situation is that the inner loop is a feedback around an actuator. The reference

in the inner loop can represent a physical quantity, like flow, pressure, velocity, etc., while

the control variable of the inner loop could be valve pressure, control current, etc. This

is also a good way to linearize nonlinear characteristics. After choosing the secondary

measured signal, the appropriate control modes for the primary and secondary controllers

have to be chosen, and their parameters have to be tuned. The choice is based on the

dynamics of the process and the nature of the disturbances, but it is difficult to give

general rules because the conditions can vary significantly.

5.4 Mid-Range Control

The dual situation of the cascade control is when there are several control signals but only

one measured signal. The two control signals can be used one at a time or simultaneously.

In the first case the control strategy is called split-range control and it will be described in

the next section. When the two control signals are used at the same time, a common situ-

ation is mid-range control or mid-ranging. The problem treated by mid-range control can

be illustrated as in Figure ADD FIGURE 12.9 PAG 379 + REFERENCE , where two valves

are used to control a flow. The valve v1 is small but has a high resolution, whereas the

valve v2 is large but with a low resolution. When small disturbances act on the system,

one controller that manipulates valve v1 is able to manage the control problem. When

39

5.5. Split-Range Control

larger disturbances occur, valve v1 will saturate, thus valve v2 must also be manipulated.

A block diagram of the mid-range control strategy is given in Figure ADD FIGURE 12.11

PAG 379 + REFERENCE , where the process P1 (valve v1) and controller C1 form a fast

feedback loop. Controller C1 takes the set point ysp and flow signal y as inputs and ma-

nipulates the valve v1. The mid-ranging controller C2 takes the control signal from C1

as input and tries to control it to a set point usp by manipulating the valve v2. If both

controllers have integral action, the flow will be at the set point ysp and the small valve

v1 will be at the set point usp in steady state.

5.5 Split-Range Control

In split-range control the two control signals can be used one at a time, or, in other words

two (or more) actuators are active in different parts of the control range. The principle

of split-range control is illustrated in Figure ADD FIGURE 12.13 PAG 381 + REFERENCE ,

where a system for heating and cooling is represented. In such a system one device is used

for heating and another one for cooling. In Figure ADD REFERENCE to 12.9 PAG 379 +

the static relation between the measured variables and the control variables is shown.

When the temperature (the measured variable) is zero the heater has its maximum value

as the environment needs to be heated. Then it decreases linearly until mid-range, where

the heater stops to act. When the process variable (the temperature) is above mid-range,

cooling is applied, and then it increases. The problem with mid-range is that there is a

critical region when switching from heating to cooling, also because the switching may

cause oscillations. To avoid to have both heating and cooling at the same time, there is

often a small gap where neither heating nor cooling is supplied. Spit-range control is also

useful when the control variable has a large range of variation, in this case the flow is

separated in parallel paths, each controlled with a valve.

5.6 Ratio Control

Ratio control is a strategy that allows control of two process variables so that their ratio

is constant. The block diagram of a typical ratio control strategy is shown in Figure ADD

FIGURE 12.17 PAG 385 + REFERENCE , in which the main loop consists of process P1 and

controller C1. The main flow is the output y1 of the process P1, and r1 is the set point

for y1. The second loop consists of process P2 and controller C2. It is used to control the

40

5.6. Ratio Control

flow y2 so that the ratio y2

y1is equal to a desired constant a. This is obtained applying a

Ratio statio (RS) to the main flow y1, where the set point r2 is determined by

r2(t) = ay1(t) (5.8)

The desired ratio a can be also time-varying.

ADD CONSIDERATION ABOUT NESTED STRUCTURE

41

Chapter 6

Scheduling of Wireless Control

6.1 Introduction

Networked control systems are spatially distributed systems for which the communication

between sensors, actuators, and controllers is supported by a shared communication net-

work. When considering distributed networked control, the network must be considered

explicitly as it affects the dynamic behavior of the control system. Thus, the design of the

control system has to take into account also the presence of the network as it represents

the interconnection between the plant, the controller, and the sensor and actuator compo-

nents. With shared communication resources (i.e. the wireless medium) the transmission

of sensor measurements or actuator signals may not be immediate and communication

delays and packet losses may occur. Moreover, using wireless communication introduces

new issues such as fading and time-varying throughput in communication channels.

As a result of these changes, new concerns have to be addressed when designing con-

trol systems. This is the reason why several areas such as communication protocols for

scheduling and routing have become important in control when considering, for example,

stability, performance, and reliability. Thus, algorithms and software that are capable of

dealing with hard and soft time constraints are very important in control implementa-

tion and design, and areas such as real-time systems from computer science are becoming

increasingly important also in control theory.

In this work we consider a plant automation network made up of several sensors and

actuators connected to an AC800M controller through a gateway. The aim of the project

is to find a good scheduling algorithm in order to manage the exchange of information

between sensors/actuators and the gateway and between the gateway and the controller.

42

6.1. Introduction

The use of a multipurpose shared network to connect spatially distributed elements re-

sults in flexible architectures and generally reduces installation and maintenance costs. In

industrial environments costs can be further reduced and productivity can be improved by

means of wireless sensor and actuator networks, which give numerous benefits with respect

to wired networks. However, wide deployment of wireless industrial automation requires

substantial progress in wireless transmission, networking and control because an industrial

application will frequently require hard bounds on the maximum delay allowed. In par-

ticular, as the sensors and actuators are part of closed loop control systems, strict timing

requirements apply, ensuring a short response time and an efficient use of the available

radio bandwidth. The most prevalent network topology in industrial environments is the

star topology, where wireless nodes communicate with a gateway device that bridge the

communication to a wired network. Industrial systems are functionally and, in most cases,

also physically built in levels. The system can be separated into the following network

areas 1 on different levels (as shown in Figure 6.1):

• plant network: it can be dedicated for process automation purposes;

• client/server network: it is used for communication between servers, and between

workplaces (clients) and servers.

• control network: it is a Local Area Network (LAN) optimized for high performance

and reliable communication, and with predictable response times in real time. Con-

trollers are connected to the control network.

It is important to notice that for large systems it is recommended to separate the control

network from the client/server network. Actually, the two networks should be separated

also for smaller systems, because this solution allows fault isolation between the two net-

works and traffic filtering, as the client/server network’s traffic will not disturb the real

time traffic on the control network. If in the network there are more than fifty nodes, it

is recommended to split the control network on two or more network areas, maintaining

the communication between controllers within one network area [4].

NOTE: IN THIS CHAPTER... + CASE STUDY DESCRIPTION1A network area is a logically flat network that does not contain IP routers.

43

6.2. Scheduling problem statement

Figure 6.1: Industrial network topology [13].

6.2 Scheduling problem statement

Consider a physical plant interacting with a controller that measures some plant signals

and generates appropriate control signals in order to influence the behavior of the plant.

The industrial plant is made up of several sensors and actuators being connected to a gate-

way, which, in turn, communicates with a controller. Sensors and actuators (field devices)

and the gateway are part of a WirelessHART network (see Chapter 2) and the controller

is supposed to be an AC800M (see Chapter 4). According to the WirelessHART stan-

dard the gateway communicates with the network manager, that has network management

functions, via a secure communication channel. In addition, the gateway is connected to

the AC800M through a high speed fieldbus. First, a star network topology is considered,

where each field device is directly connected to the gateway, so each sensor or actuator is

one-hop away from the gateway. Later, a multi-hop WirelessHART network will be taken

into account.

This industrial plant can be represented by means of a set of control loops, where each

control loop has a certain number of input and output signals. Input signals are sensor

measurements that the controller uses to generate actuator signals (output signals). Each

control loop can be seen as a black-box: only its scan rate is supposed to be known,

44


that is the sampling period of the system or, in other words, the update rate that the

sensors connected to that system use to send the respective measurements. The considered

environment is static and does not change in time so an off-line scheduling algorithm is a

good choice in order to capture the entire scheduling in a static table. Moreover, an off-line

algorithm allows to have predictability and to have a simple dispatcher (see Subsection

3.2.2).

Each control loop is supposed to be a task that has to be executed (see Chapter 5 to have

more details about control loops). A task is a set of jobs. There are two classes of jobs,

according to which kind of resources they share

• communication jobs (shared communication resources);

• control jobs (shared computation resources).

A communication job can be either a sensor signal or an actuator signal. For each task

there is a control job, meaning that the gateway receives the process measurements from

sensors, and sends them to the controller that makes the computation. After that the

controller transmits the results of the computation to the gateway that sends the output

signals to the actuators. Suppose to consider a simple PID controller as shown in Figure

6.2. This control loop/task is made up of two communication jobs and one control job.

Figure 6.2: PID controller.

The sensor signal S1 and the actuator signal A1 are the communication jobs, while C1 is

the control job. It is assumed that the rate with which sensors send their measurements

is the same as the frequency with which actuators receive control signals. For each task

this scan rate is supposed to be known.

Consider now as a general case the control loop represented in Figure 6.3. It is the k -th

task/control loop of the plant and it has a set of nk input signals (sensor measurements)

{Si}, i = 1, . . . , nk, and a set of mk output signals (actuator signals) {Aj}, j = 1, . . . ,mk.

There is only one control task Ck that makes the computation of the control signals for the

actuators. The task has an update rate of Tkms. The plant is supposed to be constituted

by a set of control loops identified by {Ck}, k = 1, . . . , p.

45


Figure 6.3: Example of a generic control loop.

In a WirelessHART network shared resources are scheduled in terms of time slots and su-

perframes, and according to the WirelessHART specification, time slots of 10 ms are used

(see Subsection 2.3.1). Hence, in a star topology the communication between a sensor and

the gateway requires only one time slot. The same applies to the communication between

the gateway and an actuator. Concerning the time required to complete the control job,

we have assumed that within one time slot the gateway sends the measurements to the

AC800M via a high speed fieldbus, the controller makes the computation and sends the

results back to the gateway by means of the same fieldbus (see Chapter 4 and the 800xA

documentation for more details).

6.2.1 Superframes setting

Suppose to consider a known fixed control loops set, with known periods and known exe-

cution times (of communication and computation jobs). For the sake of presentation it is

assumed to assign a superframe for each period and then to combine all these superframes

in a unique superframe having the same size of other superframes, called gigaframe. Thus

each superframe corresponds to a period and all periods should follow a harmonic chain,

i.e. all periods should divide into each other (see Subsection 2.3.5). It is important to no-

tice that all superframes, including the gigaframe, have the same size: it is defined as the

least common multiple of all periods of the control loops set. It has been described above

that there are two types of jobs to be scheduled, communication jobs and computation

jobs. Hence, two kinds of superframe are defined:

• communication superframes (to be used for communication jobs);

• control superframe (to be used for control jobs).

6.2.2 Communication and control superframe schedule

As said above, the communication from sensor to gateway or from gateway to actuator

occurs within a time slot of 10 ms. So, if the general case system of Figure 6.3 is considered

46

6.3. Formalization of the scheduling problem

as an example, first a slot has to be reserved for each one of the jobs belonging to the

set {Si}, i = 1, . . . , nk, and then to the set {Aj}, j = 1, . . . ,mk, meaning that, if a slot

is occupied by Si, in that slot the communication between that sensor and the gateway

occurs, or, alternatively, if it is occupied by Aj then in that slot the gateway communicates

with that actuator. For a given task, one slot in the communication superframe has to be

reserved for each element in the set of sensor signals, and for each element in the set of

process actuators. A formal definition of the scheduling problem will be described in the

next section.

6.3 Formalization of the scheduling problem

The scheduling theory has been deeply explored in the academic literature and has pro-

gressed in recent years due to the strict requirements of real-time systems such as pre-

dictability and timing constraints (a review of the most important algorithms can be

found in [6]). However, extending this scheduling theory in practice is not so easy. For

example, a theoretical approach to schedule periodic tasks is the RM (Rate Monotonic)

algorithm (see Subsection 3.2.1 and [7]). According to RM, all tasks are periodic, do not

synchronize with one another, do not suspend themselves during execution, ca be instantly

preempted by higher priority tasks, and have deadlines at the end of their periods. The

term ”rate monotonic” originated as a name for the optimal task priority assignment in

which higher priorities are accorded to tasks that execute at higher rates (that is, as a

monotonic function rate).

These ideal assumptions do not fit well with a real environment in which the notion of

task is often completely different from the one defined in theoretical algorithms. In these

cases the implementation of a new scheduling algorithm is required. It can be developed

over the base of an already existing algorithm or it can be implemented from scratch.

In this work a suitable model of tasks and jobs is given (see Section 6.2), according to the

requirements of a typical industrial application, as the one described in Section 6.2. This

definition is required in order to schedule the shared communication and computation

resources.

After this first step the development of a new scheduling algorithm is needed. In particular,

it is important to select the jobs to be scheduled with the purpose of having a feasible

solution. In addition, jobs have to be allocated in a proper way in order to achieve good

47


performance and quality of service in terms of delay and robustness of the network.

To preliminary study the performance of a scheduling algorithm a feasibility analysis is

usually used (see [6] for further information). Feasibility analysis refers to the process

of predicting temporal behavior via tests, which determine whether the temporal con-

straints of tasks will be met at run time. Such analysis is important when implementing a

scheduling algorithm because the predictability is one of the key requirements of real-time

systems. Thus, it is fundamental to specify, understand, analyze and predict the timing

behavior of a scheduling algorithm used in a real-time context. For many important task

models and scheduling algorithms, feasibility analysis is provably computationally very

expensive, and in some cases even intractable. Moreover, in some cases the current gener-

ation of schedulability analysis tools offer inadequate support for new algorithms. Thus,

when a new algorithm is proposed, a way to verify the feasibility of this new approach

should be indicated.

As described above, in the considered WirelessHART network shared resources are sched-

uled in terms of time slots and superframes. In this work there are two types of jobs to be

scheduled, communication jobs and computation jobs, according to which kind of resource

they share (communication or computation resources). The timing of this basic operations

is critical to the performance of the controller, and it must be possible to schedule all the

control loops of the plant. Thus, two important constraints have to be fulfilled:

• the proposed scheduling algorithm must produce a schedulable solution, i.e. both

communication and control jobs must be allocated in the respective superframes;

• the timing of the input and output actions, and of the control computation of each

control loop must be strictly satisfied as it is critical to the performance of the control

loop.

The formulation of the problem of finding a scheduling algorithm fulfilling these constraints

can be stated as

s : T 7→ Σ (6.1)

where s is the particular scheduling algorithm in the set Σ of all the possible schedules,

given the temporal space T of time slots and superframes.

Assume that S ⊂ Σ is the set of all possible scheduling algorithms that gives a schedulable

solution, i.e. S ⊂ Σ is the set of all possible feasible solutions to the given scheduling

problem. Thus, to respect the constraints on the temporal behavior of the considered

48


real-time system, the scheduling problem can be formulated as a constrained optimization

problem. The objective is to find a scheduling algorithm s ∈ S such that

mins∈S

F (s)∈F

D(s) (6.2)

where F (s) ∈ F is the set of restrictions for the particular scheduling algorithm s, belonging

to the set of all possible restrictions F, and D(s) is the constraint on the temporal behavior

of the real-time system.

In particular, the aim of the proposed scheduling algorithm is to minimize the time delay

from sensing to actuating for each control loop (and then over the set of all control loops).

The search of the minD(s) is subject to restrictions F (s) in the set F of all possible re-

strictions. F (s) is made up of constraints on communication and control jobs as described

below:

• each communication job requires a time slot to be scheduled,

• a device can only transmit or receive in one time slot,

• each control job requires a time slot to be scheduled,

• for each control loop, the control job can be executed only after that all jobs belong-

ing to the sensor signals set are scheduled.

Finding the optimum solution to this minimization problem has a non-trivial complexity

as it requires an exhaustive search in the space of feasible solutions. In optimization

problems, in order to avoid an exhaustive search, enumerative methods could be used to

reduce the number of solutions that need to be explored [12], or approximation methods

can be exploited to find a near optimal solution with moderate computing time.

The optimization problem stated above corresponds to the problem of finding a perfect

solution for combining communication and control jobs in such a way that the feasible

solution is always obtained and the delay between from sensing to actuating is minimized.

Finding a perfect solution is a hard problem. However, in this work a heuristic algorithm is

proposed, as theoretically deriving an optimal off-line scheduling satisfying the minimiza-

tion problem is an intractable problem under all realistic assumptions and restrictions.

To evaluate the performance of the proposed algorithm, in terms of delay and feasibility,

a random analysis will be performed instead of using a pure deterministic approach (see

Section ?? where the random approach is described in detail).

The detailed scheduling policy will be described in the next subsection.

49

6.4. Scheduling policy

6.4 Scheduling policy

Having defined a superframe for each scan period (see Subsection 6.2.1), we suggest to

schedule communication and control jobs allocating slots starting with the fastest to the

slowest scan rate, as in the RM scheduling policy (see Subsection 3.2.1). The proposed

algorithm allocates slots starting with the sensor signals of a given task. Supposing to

consider the k -th task the scheduler assigns a slot to each signal belonging to the set {Si},

i = 1, . . . , nk. After that it leaves at least one free slot in the communication superframes

and assigns the respective slot of the control superframe to the control job Ck. It is

reasonable to assume that within this time slot the gateway sends the sensor signals {Si},

i = 1, . . . , nk, to the controller, via a PROFIBUS, a Modbus or a FOUNDATION Fieldbus

(see Section 4.5). After the control task has been executed the controller sends the output

signals to the gateway. At this point, the gateway has to transmit these signals to the

actuator and, in order to do so, the algorithm allocates a time slot for each element of the

set {Aj}, j = 1, . . . ,mk, meaning that, if a slot is reserved for Aj , then in that slot the

gateway sends the output signal Aj to the respective actuator.

In a given superframe one or more tasks has to be scheduled, so a partial ordering between

them is required. They can be ordered according to the number of jobs (both commu-

nication and control jobs) and/or to the cardinality of the sensors set. In particular, in

a given superframe (i.e. for a given period) we propose to allocate tasks from the one

with the lowest number of jobs to that one with the highest number of jobs. This choice

is assumed to reduce the time delays from sensing to actuating over the set of control

tasks (see Subsection ?? for more details about performance evaluation of the proposed

algorithm). For tasks with equal number of jobs, in order to increase the chance of having

a feasible solution of the scheduling problem, tasks with a higher number of sensors will

be given a higher priority compared to tasks with a lower number of sensors. This means

that, when equal number of jobs, tasks should be allocated from the highest cardinality of

the sensor set nk to the lowest one. In the following subsections three different scenarios

are described requiring some additional considerations.

6.4.1 Scenario I

In the first scenario the following assumptions are considered:

• all tasks are independent, that is, have no precedence relationship;

50


• the WirelessHART network has only a gateway with one access point.

A completely independent task set allows to consider only the partial order of the commu-

nication jobs (for a given task sensors must be scheduled before actuators) instead of any

relation between control jobs. They can be scheduled according to the partial order defined

by the number of jobs and the number of sensors. The algorithm creates the gigaframe of

length equal to the least common multiple lcm(Tk), k = 1, . . . , p of the periods of all tasks

of the plant (see Subsection 2.3.5). Then for each period it creates a superframe of length

equal to the gigaframe size. Slots are allocated from the lowest to the highest period.

Suppose to consider the lowest period Tl. The algorithm starts allocating slots according

to the rule described above, i.e. with respect to the number of jobs and, when equal number

of jobs, with respect to the number of sensors. After that this basic frame of length equal

to the period Tl is repeated for a number of times equal to lcm(Tk)Tl

, thus forming the whole

superframe for that period. At this point, the algorithm allocates jobs for the next period

Tl+1 following the same rule.

However, at this step there is an additional constraint because jobs cannot be allocated in

the slots occupied by jobs of the previous superframe, i.e. the superframe corresponding to

the period Tl. As done for the lowest period, the obtained basic frame is repeated lcm(Tk)Tl+1

times. Assume now to consider the i-th period Ti, with 1 < i ≤ p. The scheduler allocates

jobs of tasks having period equal to Ti in the superframe corresponding to Ti, creating

first a basic frame and then repeating it for a number of times equal to lcm(Tk)Ti

. However,

jobs cannot be allocated in slots already occupied by jobs of all the previous superframes.

In other words, when allocating jobs in the superframe corresponding to the period Ti, the

algorithm cannot use slots that are already occupied in one of the previous superframes,

i.e. superframes corresponding to the periods from the lowest one Tl to Ti−1, that is Tk,

k = 1, . . . , i− 1.

Both the communication jobs and the control jobs have to be scheduled. Suppose to

consider the k -th task. If the first actuator A1 of the task cannot be put in the next

adjacent slot of the corresponding control job Ck because that slot is already assigned to

another communication job, then the control job Ck will have a window in which it can

be scheduled. It is proposed that the algorithm decides which slot of the window has to

be assigned to Ck according to considerations about the delay it could generate over other

control jobs (having higher priority) in the case it will create some jitter. In particular,

if possible, it will be scheduled in a slot such that it will have a free successor slot in the

51


control superframe, otherwise it will be assigned the first available slot. This procedure is

applied for all periods, until the highest one.

Following this approach it could happen that a certain superframe is not schedulable,

i.e. not all communication jobs can be allocated in the communication superframe. If

this is the case, the algorithm makes a ”shuffle” of the last communication superframe

(the highest period superframe). With the shuffle function, the algorithm shuffles the

last sensor of the superframe with the first previous actuator, then it schedules all the

sensors belonging to the same sensor set of the shuffled sensor, the shuffled actuator and

the actuator set belonging to the same task of the shuffled sensor. If the last superframe

contains only one task this action cannot be done because the superframe contains all the

sensors and then all the actuators. In this case, the algorithm makes the shuffle between

the last superframe with the previous one, shuffling the last sensor of the last superframe

with the first previous actuator in the previous superframe. Then it re-schedules the

last superframe and updates the control superframe taking into account the changing in

the last superframe and in the previous one. It is important to notice that, if at first

a schedulable solution is not produced, a feasible solution is always obtained after this

shuffling operation.

Once all superframes are created they can be combined together in the gigaframe, as

slots are not occupied by more than one job. Thus, at the end of the algorithm the

communication gigaframe and the control superframe are available. They can be stored

in the scheduler as tables to allow it to make scheduling decisions during run-time.

Example I.1

Consider the set of control loops in Figure 6.4.

Figure 6.4: Set of control loops Example I.1.

The first control loop C1 is a simple PID controller and it has the following characteristics:

52


• update rate T1 = 50ms;

• sensor signals set {S1};

• actuator set {A1}.

For the second control loop C2 (a cascade controller):

• period T2 = 100ms;

• sensor signals set {S2, S3};

• actuator set {A2}.

The last control loop C3 (a mid-range controller) has:

• period T3 = 100ms;

• sensor signals set {S4};

• actuator set {A3, A4}.

Thus, two superframes are defined, one for each scan period, and the algorithm creates the

gigaframe of length equal to the least common multiple lcm(Tk), k = 1, . . . , 3, i.e. equal to

100ms. It allocates slots starting with the fastest to the slowest scan rate, thus allocating

first jobs of task C1. The scheduler assigns a slot in the superframe corresponding to

50ms to {S1}, then it leaves a free slot in the communication superframe and assigns the

respective slot of the control superframe to the control job C1. In this slot the gateway

sends the sensor measurement to the controller, the controller makes the computations

and sends the output signal to the gateway. Hence, the gateway has to transmit this

signal to the actuator and, in order to do so, the scheduler allocates a time slot in the

communication superframe to {A1}. This basic frame is repeated lcm(Tk)T1

= 10050 = 2 times

within the superframe corresponding to 50ms.

After that the proposed algorithms has to allocate tasks with the highest period. There are

two tasks having period equal to 100ms, and both of them have 3 jobs. When equal number

of jobs, tasks with a higher number of sensors are given a higher priority compared to tasks

with a lower number of sensors. Thus, the algorithm schedules tasks according to the order

{C2, C3}. This means that the scheduler has to assign a slot to {S2} in the superframe

corresponding to 100 ms. The first slot is already assigned to the communication between

53


{S1} and the gateway, so the second slot is allocated to {S2}. After that {S3} is allocated

to the fourth slot, as the third one is allocated to {A1}. Then the algorithm must leave

at least a free slot in the communication superframe and assigns the respective slot of the

control superframe to the control job C2. However, leaving only one free slot, the actuator

signal A2 should be allocated in a time slot previously allocated to S1 (the sixth slot in

the superframe). This means that A2 is allocated in the first free time slot after the sixth

(in this case the first free time slot is the seventh), and the control job C2 has a window

within which it could be allocated in the control superframe (the fifth and the sixth time

slot).

At this point the algorithm can allocate jobs of the last task. The scheduler can allocate

the first job S4 in the first available time slot, that is the fifth slot. Then it has to allocate

the actuator signals A3 and A4 in the last two time slots. In this way C3 has a window

in the control superframe, that is it can be allocated in the sixth, seventh or eighth slot.

Thus, the gigaframe is obtained combining the superframe corresponding to 50ms to that

one corresponding to 100ms. After the scheduling of all tasks in the communication super-

frame, the position of control jobs having a window can be fixed in the control superframe.

The control job C2 is allocated in the first slot of his window in order to leave a free slot

before control job C1, which have higher priority than C2. This is done to avoid that a

possible delay of the execution of C2 creates some jitter in the task with higher priority.

Control job C3 is allocated in the eighth slot (the last slot of its window) for the same

reason. The communication superframe, the gigaframe, and the control superframe are

all shown in Figure 6.5.

Example I.2

Consider the control loops set in Figure 6.6, where there are a PID controller having

a period equal to 50ms, and two cascade controllers with period equal to 100ms. The

algorithm follows the procedure described in the Example I.1. However, in this case, the

last superframe (corresponding to 100ms) is not schedulable, i.e. not all communication

jobs can be allocated in the communication superframe. In particular A3 cannot be

allocated in that superframe. To solve this problem, the algorithm shuffles the last sensor

of the superframe (S5) with the first previous actuator (A2). Then it schedules the shuffled

actuator (A2) and the actuator corresponding to the shuffled sensor (A3). After this

54


Figure 6.5: Superframes Example I.1.


shuffling operation the schedulable solution is obtained and control tasks with a window

can be allocated in the control superframe, making the considerations concerning the delay

described in the Example I.3. The communication superframe, the gigaframe (GF), and

the control superframe are shown in Figure 6.7.

Example I.3

In this example the set of control loops shown in Figure 6.8 is taken into account. The

scheduler allocates tasks according to the procedure described in the previous examples.

When it should allocate the only one task in the superframe corresponding to 240ms, a

schedulable solution is not obtained, as the communication job A2 cannot be allocated

within that superframe. The shuffling function described in the Example I.2 cannot be

applied, because the superframe contains all the sensors and then all actuators. In this

case, the scheduler makes the shuffle between the last superframe (corresponding to 240ms)

55




with the previous one (the superframe corresponding to 120ms). In particular, it shuffles

the last sensor of the last superframe (S4) with the first previous actuator in the previous

superframe (A5). Then the algorithm re-schedules the last superframe, obtaining the

feasible solution, and updates the control superframe taking into account the changing

in the last superframe and in the previous one. The communication superframes, the

gigaframe and the final control superframe are shown in Figure 6.9.

6.4.2 Scenario II

In this scenario the following assumptions are taken into account:

• tasks of the same period can be inter-dependent, that is, a task may require the

computation of another task of the same period;

• the WirelessHART network has only a gateway with one access point.

56



Inter-dependencies between tasks determine a partial order also among control jobs. The

dependencies constraints between tasks are represented by means of a precedence directed

graph, in which the edge (Cx, Cy) indicates that Cx is an immediate predecessor of Cy or,

in other words, that the control job Cy needs the computation result of Cx before being

executed. In this case, the algorithm makes the schedule following the procedure of the

first scenario. At the end of the schedule it analyzes the control superframe to verify that

control jobs are executed in the order required by the particular application, by comparing

the control superframe with the precedence graph. If this is not the case it tries to modify

the control superframe by shifting control jobs involved in the precedence relations that

have a window. If this is not possible it re-schedules also the communication superframe, by

changing the partial order between tasks such that the dependency relations are satisfied.

Also in this case, if a not schedulable solution is obtained, the algorithm makes a shuffle

of the last superframe or of the last superframe and the previous one, in the same way as

for the first scenario.

Example II.1

Consider the set of control loops shown in Figure 6.10, where the control job C2 needs the

computation result of C1 before being executed. The precedence directed graph represent-

ing this dependency constraint between tasks of the same period is shown in Figure 6.11.

57


Figure 6.10: Set of control loops Example II.1.

The algorithm schedules tasks following the procedure of the first scenario (see Subsection

Figure 6.11: Precedence graph Example II.1.

6.4.1) that has been described in the previous examples (both communication and control

jobs). At the end of the schedule it analyzes the control superframe to verify that control

jobs are executed in the required order, by comparing the control superframe with the

precedence graph. Control jobs C1 and C2 have a window within which they can be allo-

cated, thus they can be easily allocated according to the order required by the application,

as shown in Figure 6.12, where the communication superframes and the gigaframe are also

presented. It can be noted that, if the opposite order would be required, i.e. C1 needs the

Figure 6.12: Superframes Example II.1.

58


computation result of C2 before being executed, it is still possible to allocate control jobs

in the right order without re-scheduling also the communication superframes.

Example II.2

Consider now the control loops set illustrated in Figure 6.13, where C2 needs the compu-

tation result of C3 before being executed. The precedence graph is illustrated in Figure

Figure 6.13: Set of control loops Example II.2.

6.14. The algorithm makes the schedule according to the procedure described in previous

Figure 6.14: Precedence graph Example II.2.

examples. Analyzing the control superframe, control jobs C2 and C3 cannot be allocated

in the order required by the particular application. Thus, the algorithm must re-schedule

the communication superframes, by changing the partial order between tasks such that the

dependency relation is satisfied, i.e. it has to schedule the third task and then the second

one. As in this case a schedulable solution is not obtained, the scheduler makes a shuffle of

the last communication superframe as described in the Example I.2 (see Subsection 6.4.1).

All the superframes are shown in Figure 6.15.

6.4.3 Shared Slots

As described in Subsections 2.3.1 and 2.3.3, in a WirelessHART network links may be

dedicated or shared. In particular, using shared links may be suitable when throughput

requirements of devices are low, or when the traffic rate is irregular. In the scheduling

59


Figure 6.15: Superframes Example II.2.

algorithm proposed in this work the user can determine the percentage of shared slots in

the gigaframe. The scheduler works as follows. The user chooses the percentage of shared

slots, according to the requirements of the particular application. Then the algorithm

creates a superframe for each period of the control loops set (as described in Subsection

6.4.1), and in each superframe it reserves a number of shared time slots corresponding

to the percentage defined by the user. Suppose that the set of the periods of all tasks

contains p elements, and to indicate them as Tk, k = 1, . . . , p. Assume then that the size

of the gigaframe is equal to

TMAX =lcm(Tk)

10(6.3)

where lcm(Tk) is the least common multiple of the periods of all tasks. If Sh% is the

percentage of shared slots, the corresponding number of shared time slots ShN can be

calculated with the following expression

ShN = xq, x =[TMAX

Sh%

100

](6.4)

The notation xq indicates that the quantity x has to be round up to the next integer multi-

ple of 2p−1, if p is the cardinality of the set of the periods of all tasks. This approximation

is required to have a uniform distribution of shared slots within the gigaframe, because

the sizes of superframes should follow a harmonic chain as specified by the WirelessHART

60


standard (see Subsection 2.3.5). Assume now to consider the lowest period Tl. The algo-

rithm allocates slots in the basic frame of length equal to Tl, and repeats that basic frame

for a number of times equal to lcm(Tk)Tl

thus forming the whole superframe for the lowest

period. In this basic frame the algorithm reserves a number of shared slots equal to

ShBasicN =ShN

2p−1(6.5)

Thus it equally distributes shared slots within that superframe, the superframes corre-

sponding to the other periods, the control superframe and the gigaframe. The scheduler

allocates the control job in the shared part of the control superframe as soon as all the

elements in the sensor signals set are scheduled, for each control loop. See the following

example to better understand how the scheduler works.

Example Shared Slots

Consider the set of control loops shown in Figure 6.16.

Figure 6.16: Set of control loops Example Shared Slots.

Suppose that the user requires a percentage of shared slots equal to 20%. Thus, for this

particular example p = 3, TMAX = 32, and Sh% = 20%. The algorithm calculates the

number of shared slots as it has been described above:

x =[TMAX

Sh%

100

]= 6.40

ShN = xq = 8

ShBasicN =ShN

2p−1= 2

(6.6)

After that it can schedule both communication and control jobs. The communication

superframes, the gigaframe, and the control superframe are shown in Figure 6.17.

61

6.5. Boliden Example

Figure 6.17: Superframes Example Shared Slots.

6.5 Boliden Example

Mineral processing is the processing of ores to recover minerals or metals. Processes have

to be optimized to yield the highest possible recovery with acceptable purity (grade).

Numerous steps are involved in achieving the goal of extracting minerals and metals from

ores in their purest form, they are listed in the following.

• Size reduction, to increase the surface area for high reactivity and to facilitate the

transport of ore particles. This process in in most cases wet and the product is

usually called pulp.

• Concentration of the pulp. The most common method is froth flotation, and it is

used to separate value minerals from the non-valuable gangue.

• De-watering, where solids must be separated from water to product minerals or

metals, when the mineral processing operations are conducted in the presence of

water.

A more detailed description can be found in [24] and [19].

The general approach in the mineral industry is to use several consecutive flotation cells

to form a flotation bank. The first cells that the pulp enters in a flotation circuit are called

roughers, and the last flotation bank is the scavenger, which produces a concentrate with

a lower grade than the first cells. In the following a simplified description of the flotation

process will be given. The separation of minerals in froth flotation depends primarily on

the differences in the hydrophobicity 2 of the particles, as they must selectively attach2The term hydrophobicity refers to the physical property of a molecule to repel a mass of water.

62


to air bubbles to be floated. Some minerals can be directly floated, but in most cases

reagents have to be added to make the flotation process possible. There are three main

types of reagents:

• collectors, which make mineral particles hydrophobic;

• frothers, which keep the mineral-loaded bubbles stable;

• regulators, which make collectors more selective toward a specific mineral.

One of the most common manipulated variables that are available for control of the flota-

tion process is the addition rate of reagents. Chemical pH-regulators can also be added to

control the concentrate grade and the recovery of valuable minerals.

Consider the diagram of froth flotation cell in Figure 6.18. The flotation cell is a tank with

Figure 6.18: Diagram of froth flotation cell.

a pulp feed, an outlet and froth launders to recover the concentrate. The cell also has an

impeller to agitate the pulp and an air feed which is added close to the impeller. The air

flow rate is the main control variable affecting the amount of concentrate produced in the

cell. It offers the finest control of flotation cells. In particular, it has the quickest response

in the cell, when compared to the other control variables. The pulp level of the cell can

also be controlled, allowing to control indirectly the froth phase depth.

The Garpenberg process plant is designed to produce four concentrates: zinc, copper, lead

and precious metals. In this work the zinc flotation process is considered, as the zinc is

the most important metal extracted from the Garpenberg site’s ore.

The flow chart of the zinc flotation circuit is shown in Figure 6.19. The pulp enters the

circuit from the feed source, then it is mixed with a recycle stream in the mixer and fed

into the rougher cells. The tailings of the rougher cells are fed into the scavenger cells,

63


Figure 6.19: Zinc flotation circuit [24].

which tailings, in turns, are the waste of the circuit. The zinc flotation plant has three

cleaning stages, which purpose is to increase the grade of the concentrate. The tailings

from the first cleaning stages (FA101 and FA102) are mixed with the scavenger concentrate

and this mixture is the input of a hydrocyclone which is used for centrifugal separation

of solids and gases from liquids. The coarser particles are reground in a mill prior to

being recycled into he rougher, while the finer particles are recycled immediately. Water

is added in the concentrate launders of each cell to facilitate the concentrate flow, but the

greatest part of water is added in the cleaners to decrease the solids fraction and increase

the selectivity. All the cells in the circuit have separate air feeds with flow measurements

and control. There are also pulp level control possibilities in each cell (but when cells are

paired into banks, there is only one level control setpoint for each bank).

The three main control variables of the plant are the air flow rate, the froth phase depth

(by means of the pulp level) and the reagent addition rate. However, as the flotation is a

complex process, there are also other control variables that have to be taken into account.

All the controlled variables are listed in Table A.1, where Ts is the sampling interval.

Each controlled variable represents a control loop, i.e. in the plant there is a number of

control loops equal to the number of controlled variables. Each control loop has a PID

controller, with one sensor signal and one actuator signal. However, there are dependencies

between tasks having the same period. Thus this example can be scheduled following the

procedure of Scenario II described in Subsection 6.4.2. In particular, the recycle load

output is used as feed-forward signal to the first pulp level control loop of the rougher

64


(FA302 LC1). In addition, PU136 FC1 and PU131A FC1 (recycle load) send the setpoint

to the air flow control loops of the cleaner (i.e. FA101 FC1, FA102 FC1, FA103 FC1 and

FA104 FC1).

Following the algorithm described so far, the scheduler creates one gigaframe of length

equal to 8s (i.e. the least common multiple of the periods of all tasks of the plant) and

four superframes, one for each period (1s, 2s, 4s, and 8s), of length equal to the gigaframe

size. Then slots are allocated from the lowest to the highest period.

ADD SHARED SLOTS AND DELAY AND CONTROL LOOPS IN SHARED SLOTS

65

Chapter 7

Conclusions and future works

The proposed scheduling algorithm is proved to be a good solution to meet the feasibility-

delay tradeoff especially when the number of simulations increases, that is a very high

number of control loops is scheduled. However sometimes off-line strategy is not the most

suitable solution. This scheduling policy is not flexible enough for handling dynamic sit-

uations. In fact, a creation of a new task, or a change in a task rate, might modify the

values of the minor and major cycle, thus requiring a complete re-design of the scheduling

table. Run-time flexibility is a desired feature, as not all run-time events can be com-

pletely taken into account in advance. In addition off-line scheduling is fragile during

overload situations, since a task exceeding its predicted execution time could generate a

domino effect on the subsequent tasks, causing their execution to exceed the minor cycle

boundary. Another potential source of inefficiency is the need to fit all activities into

common multiples of the major and minor cycle (so if an activity does not fit exactly into

the schedule it may be necessary to rewrite the code to make it fit).

Theoretically deriving an optimal off-line scheduling satisfying the delay minimization

problem is a hard problem under all realistic assumptions and restrictions, which means

that in the worst case an exponential amount of work appears necessary to determine

whether a feasible solution exists. In other words, in the worst case an exhaustive search

in the space of feasible solutions is necessary in order to determine if a schedulable solution

exists or not.

The performance of the described algorithm has been evaluated by simulations but it

has not been proved that this scheduling always leads to an optimal solution. Thus an

analytical formalization of the algorithm is required to confirm the experimental results

66

obtained.

67

Appendix A

Boliden Control Variables

68

Table A.1: Controlled variables for the Garpenberg plant.

LOOP CATEGORY Number of loops Loop name Ts

AIR FLOW 9 FA301 FC1 2

FA302 FC1 2

FA303 FC1 2

FA304 FC1 2

FA305 FC1 2

FA101 FC1 2

FA102 FC1 2

FA103 FC1 2

FA104 FC1 2

PULP LEVEL 6 FA302 LC1 2

FA303 LC1 1

FA305 LC1 8

FA102 LC1 8

FA103 LC1 8

FA104 LC1 8

pH 3 FA301 AC1 1

FA304 AC1 1

FA102 AC1 1

PUMP LEVEL 5 PU230A LC1 2

PU231 LC1 1

PU131A LC1 8

PU136 LC1 4

PU006A LC1 1

WATER CONTROL 2 FA100 FC1 1

FA300 FC1 1

REAGENTS 2 BL031 FC1 2

FA300 FC2 1

RECYCLE LOAD 2 PU136 FC1 2

PU131A FC1 2

69

Table A.2: Garpenberg plant: variables to be scheduled in shared slots.

LOOP CATEGORY Number of loops Loop name

MOTOR ON/OFF 13 FA301

FA302

FA303

FA304

FA305

FA101

FA102

FA103

FA104

BL031

KV6

PU210

PU211

HAND VALVE INDICATOR 8 FA303 GS1

FA303 GS3

FA101 GS1

FA102 GS1

FA102 GS2

FA305 GS1

PU230A GS1

PU230B GS1

CYCLES ON/OFF 7 CY06 Px

70

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