Master Thesis Presentation

بسم هللا الرحمن الرحيم

Integrated Tools for the Analysis

and Design of Intelligent Control

System

Submitted by:

Eng. Mohamed Ali Sobh

Supervised by:

Prof. Dr. Mohamed Abd El-hamid Sheirah

Ain Shams University, Faculty of Engineering

Computer and Systems Engineering Department

Main Topics

Background

Achievements

Related Tools

Conclusion and Future Work

Build an interactive CAD tool for control

system design

Provide the tool with different libraries

Integrate most popular AI techniques within

the tool environment

Apply the tool in real world examples

Main Topics

Background

Achievements

Related Tools

Background

Modeling and Simulation

System Representation Languages

AI Techniques

Modeling and Simulation

Definition

Using computers to imitate, or simulate, the operations of various kinds of

real-world facilities or processes.

It is useful for:

• Design and analysis for industrial control systems

• Design and analysis for manufacturing systems

• Determining ordering policies for an inventory system

• Designing communications systems and message protocols for them

• Analyzing financial or economic systems

System Simulation Types

Continuous time system simulation

Event driven system simulation

Modeling and Simulation Continuous Time Simulation

This technique is used to study the dynamic behaviors of

the system, which change continuously with time

Modeling and SimulationDiscrete event Simulation

This technique used to study the dynamic behaviors of

the system, which change only whenever certain type of

events occurred

Events

Item Arrive

Begin Processing

End Processing

Behaviors/States

QL: Queue length

D: Delay in queue

W: Machine State

System Representation Languages

The growth in the complexity of modern industrial systems

creates a problem for their developers. They have to model

complex behaviors and large number of constrains using

ordinary programming techniques. For that reason many

system representation techniques and languages are

developed

Input/Output block diagram

Petri Net

System Representation LanguagesInput/Output Block Diagram

The system model represented by a set of connected

block, defining the inputs and outputs relationship.

Designer has to define the internal block model. For

example

System Representation LanguagesGPSS (General Purpose System Simulation)

It is a high level

language that used to

model and to simulate

discrete event systems,

It consists of a set of

predefined blocks that

represent different

system behaviors like

queue, service, delay,

priority, and statistics

gathering. For

example:

System Representation LanguagesPetri Net

It is a low level

language that used to

model and to simulate

discrete-event and

continuous-time

systems, It consists only

of tow predefined-

blocks [Place and

Transition], which are

enough to represent

very complex systems.

For example:

AI Techniques

Neural Network : Modeling and Learning

Fuzzy System: Decision Making

Genetic Algorithm: Optimization

Artificial Intelligent (AI) techniques, are used

to efficiently solve many control system

problems:

Main Topics

Background

Achievements

Related Tools

Achievements

Techniques and Algorithms1. Mixed simulation technique

2. Generalized Petri Net Model

3. Generalized Fuzzy System Object Model

4. New Matrix Based Language

5. Generalized model for dead time element

CAD Tool Libraries

AI Components

Applied Examples

1.Mixed simulation technique

more …

Support both continuous-time and/or discrete-event-driven systems

Based on discrete event simulation, and modified for the continues-time system case

Support Object Oriented Programming Model (Blocks are Isolated from each others, communication established by events, Blocks data and functionality are encapsulated within the block/object)

Successfully used within the tool to simulate systems represents by: Input/output block diagram

Petri-Nets

1.Mixed simulation techniqueContinuous Time Simulation Technique

Initialization

E=R-C Summer

If E>0 Then M=1ELSE M=0 On-Off

L=L+T*(K*M-L) / T Lag

Time = Time + T Time Update

Time<ET

1.Mixed simulation techniqueDiscrete event Simulation Technique

Initialization

Get Top Event

Time<ET

Time = Event Time

Process Event

Event 1

Event 2

Event 3

Event Time QL

Item Arrive 0.4 0

Begin Processing 0.4 0

Item Arrive 0.5 1

Item Arrive 0.6 2

End Processing 0.8 2

Begin Processing 0.8 1

The Simulator use modified discrete event

simulation technique to perform both

simulation types.

K1 K2tOP

Time = T1

Current Time = T1

Event Queue

E1 at T1

Event Queue

E1 at T1

Event Queue

E2 at T1

Event Queue

K1 K2tOP

Time = T1

Time = T1E 1

Time= T1+T

Current Time = T1

Event Queue

E2 at T1

E1 at T1+ T

Event Queue

E2 at T1

E1 at T1+ T

Event Queue

E3 at T1

E1 at T1+ T

Event Queue

E1 at T1+ T

• Elements activate whenever event

arrive

•Dynamic element doesn't activate

twice at the same time

• Simulation starts from inputs

• Time will advance only if the

schematic contains dynamic inputs

1 Event Queue (Time=T-T)

E (Step Input) at T

4 Event Queue (Time=T)

E (Sum2) at T

E (Step Input) at T+ T

E (Sum1) at T

E (Controller2) at T

E (Lag1) at T

E (Dead Time) at T

E (Sum2) at T

E (Lag2) at T

E (Sum1) at T

E (Scope) at T

E (Sum1) at T

E (Scope) at T

15 Event Queue (Time=T+T)

E(Sum1) at T+T

E (Step Input) at T+ 2xT

2.Generalized Petri Net Model

Support Following Petri-Nets types

Discrete and Continuous (constant or variable

speed)

Deterministic and Stochastic

Autonomous and Non-Autonomous (T or P timed)

Priority

Any hybrid combination of above

Integrated within the simulator

2. Generalized Petri Net ModelStructure

Transition

In Arc

Out Arc

Place: Type, Marking, Delay Type, Delay,

Capacity, Sampling-Time

Transition:Type, Delay Type, Delay,

Semantic Type, Sampling Time, Velocity,

Speed Type, Firing Quantity

In Arc: Weight Type, Weight, Inhibited,

Priority Level, Priority Weight

Out Arc: Weight Type, Weight

2. Generalized Petri Net ModelProposed Event Graph

Transition

Event Name Direction

1 Init Place Discrete or Continuous Place

2 Enable

Transition

Discrete Place Discrete or Continuous Transition

Continuous Place Discrete Transition

3 Reserve Marks Discrete Transition Discrete or Continuous Place

Continuous Transition Discrete Place

4 Remove Marks Discrete Transition Discrete or Continuous Place

5 Pre-Add Marks Discrete Transition Discrete or Continuous Place

6 Add Marks Discrete Transition Discrete or Continuous Place

7 Release Marks Discrete Place Discrete Place

8 Place Time

Update

Continuous Place Discrete Place

9 Transition

Time Update

Continuous Transition Continuous Transition

Features

General

Complexity wrapping

Multilevel of manipulation

Easy to use

Accuracy control

Features: General

Supported Memberships: Triangle, Trapezoidal, Pi, Beta,

Gaussian, Delta, Linear, Crisp, Points (General Case)

Support Hedge operation on memberships : Slightly,

Extremely, Very, Somewhat, Generally, Below, Positively,

Above, Close, Vicinity, About

Support logical operation (AND,OR, Not) on memberships:

Supported AND methods:Zadeh, Mean, Meansqr, Meansqrt, Product, Boundedprod, Invproduct, Logproduct, Drasticprod, Boundedsum, Yager, Dubois Prade

Support following De-Fuzzification methods : COG, COA,

Average Momentum, Absolute Momentum, Delta COG

Support alpha cut, rule weighting, rule inversion

Features: Complexity Warping

CFuzzySystem

Array of Input Variables

Array of Output Variables

Inference Object

CFuzzyVariable

Array of Memberships

CFuzzyMembership

CFuzzyRule

CFuzzyInference

Array of Rules

Features: Fast

All three representation are used, by default all memberships

are converted to discrete representation when the fuzzy

system starts the on-line mode

4. New Matrix Based LanguagePower Matrix Script

Most of operation are base on matrices

Complexity of calculation

Give the user the ability to customize

the tool. And to define new elements

and operations

Specification

Language with all structured languages features

Global variables, variables, constants

Control statement [If, for, while, switch]

Functions, subroutines

Support recursive calling

Variables and Constants are matrices or vectors

Support all matrix operations

Feature

Support 9 data types [all C data types + complex], this feature doesn't supported by Matlab script

Perform automatic data conversion

Function support any number of input and output arguments

High Error protection against user mistakes

Libraries can be extended by functions and subroutines and exported C functions

Automatically detect libraries

Integrated with the simulator

Encapsulated within single object

Definition

Dead Time

Programming Model

1 2 3 NInput OutputModel 1

Model 2

Large memory usage

Doesn't support dynamic dead time value,

required memory re-allocation for each change

Proposed Model

• Represented with ring with movable starting point

• Buffer size is limited with certain value

Proposed Model

M: maximum length of actual buffer L: Current length of actual buffer

1 2 N3 I I+R

Unused Area

1 2 L MJ

Theoretical Buffer

Actual Buffer

Unused Area

Theoretical Buffer

Actual Buffer

1 2 NI

1 2 LJ

Proposed Model

CAD Tool

Visual environment enable the user to plane and

to design different control problems

Features

• One environment support all simulation operations (design, test,

debugging, developing)

• Visual and follow standard GUI of CAD tools

• Extendable with libraries and routines, with automatic library detector

• Support parallel operations

• Custom interface, based on user needs

• Protected against user errors, and Easy to use

CAD Tool – Schematic Editor

CAD Tool – Power Matrix Interpreter

CAD ToolSystem representation within the tool

Connection 1 Element

Source = summer

Destination = PID

Connection n Element

System

Elements Array

Summer

Step Input

Summer Element

Scope Element

Step Input Element

PID Element

Connection 1

Connection …

ID = 716

Type = 102

Library = “Control”

P = 100

Connection 1 Element

ID = 611

Type = 11

Library = “Common”

Source = summer

Destination = PID

Motor Macro Element

System

System “Motor”

ID = 718

Type = 70

Library = “Common”

Elements

Dead Time

CAD ToolDetail tool structure

element i sub-system j

Schematic Editor

System

Simulator

Power-Matrix Script Interpreter

Libraries Viewer Object Inspector

Value1P1

P2 Value2

… ...

Loaded

Libraries

Lib1 Lib2 Lib n

Library

Element

DefinitionEnvironment Variables

Definition

Environment

Variables

Runtime Variables

Control CenterCommand Line Interpreter

Syntax/Script Editor

A = [1 2 3]

B= C + D

IF(A>PI/2)

B=C+D;

Others

Libraries

Input/Output blocks library Inputs

Outputs

Control Elements Linear

Non-Linear

Hardware

Petri Net120 Element

AI Techniques

Fuzzy Logic system

Neural system

Genetic

Applied Examples

1. Adaptive Fuzzy-PID Control

2. Fuzzy Control For Liquid Level Process

3. Fuzzy Control For Inverted Pendulum

Process

4. Traffic System Design Using Petri-Nets

5. Production Line of CDs Factory

6. Model-Based Fuzzy-PID Control

Applied Examples1. Adaptive Fuzzy-PID Control

Fuzzy System

Control

System: First order + Dead Time

Conventional PID tuning for optimal response

Optimization Methods

• Iterative Search

• Hill Climbing

• Random Hill Climbing

• Simulated Annealing

• Genetic

System Td=5, Tc=5, K=5

Optimal PID P=466.406, I=7.71484, D=1.69385 IAE=7.73928

Schematic

Results

Schematic to study the effect of parameter change within

certain range

Results

Applied Examples2. Fuzzy Control For Liquid Level Process

System

Applied Examples

Schematic

Applied Examples

Fuzzy Settings

Applied Examples

Results

Applied Examples

Modified Fuzzy Control

If Level is Very Very Low Then Valve is Open-fast2

If Level is Very Very High Then Valve is Close-fast2

Applied Examples

Results

Applied Examples

3. Fuzzy Control For Inverted Pendulum Process

Process

Applied Examples

Fuzzy Settings

Velocity

Applied Examples

Schematic

T(1)=H(2); //velocity

T(2)=(4*(f-mu*H(2))-

1.5*bm*g*sin(2*H(3))+4*bm*L*H(4)^2*sin(H(3)))/(4*(TM+bm)-

3*bm*(cos(H(3)))^2); //acceleration

T(3)=H(4); //angular velocity

T(4)=(3/(4*L))*(g*sin(H(3))-T(2)*cos(H(3)));//angular acceleration

…K2=T;

…K3=T;

…K4=T;

D=D+(K1+K2*2+K3*2+K4)*(DELTAT/6);

x=D(1); v=D(2); a=D(3); w=D(4);

Inverted Pendulum Model using Power Matrix Script

Applied Examples

Results

Applied Examples

Traffic System Components - Petri Net Model

Road Segment

Applied Examples4. Traffic System Design Using Petri-Nets

PI Intersection

Applied Examples

U-Turn

Applied Examples

Square

Applied Examples

System Model

2 traffic Intersections using Squares

Applied Examples

System Model

2 traffic Intersections using U-Turns

Applied Examples

Results

Input car generators [T=10,W=1] for each road

Simulation Time: 10000 second

Results:

Model 1 Model 2

Average Queue Length 3.067 0.1748

Average System Capacity 61.77 15.5

Case Study 1: Low Traffic

Input car generators [T=2,W=1] for each road

Simulation Time: 10000 second

Results:

Model 1 Model 2

Average Queue Length 466 0.8722

Average System Capacity 7500 77.9308

Case Study 2: Heavy Traffic

Applied Examples

• Factory contains following production lines

• [Disk, Upper Cover, Down Cover, Holder ] Lines

• Assembly and Covering Line produce complete CD

• Packaging into small Boxes Line

• Packaging into Large Cartons Line

• Lines separated by buffers

• Input martial [Plastic Powder, Tin , Plastic Cover, Labels,

Small Boxes, Large Cartons] is stored in input buffers

Applied Examples

GPSS - Model

Applied Examples

Case Study "Study the effect of Tin level update quantity"

Tin level update = 100 Kg/Day

Simulation Time 300,000 second (about 3 days)

Results:

Average Level of Tin material: 46.244

Maximum Level of Tin material: 200

Average Length of Tin Covering Queue: 547.147

Maximum Length of Tin Covering Queue: 2700

Production Rate: 9039/300000 108.468 CD/hour

Case Study

Applied Examples

Case Study "Study the effect of Tin level update quantity“

Tin level update = 100 Kg/Day

Rate of change of Tin material level

= - 0.0020544 kg/sec

The required level update every date

= 0.0020544*24*60*60 = 177.5 KG

Conclusion

Applied Examples

Case Study "Study the effect of Tin level update quantity“

Tin level update = 177.5 Kg/Day

Applied Examples

Case Study "Study the effect of number of assembling units

on production "

Number of assembling machines = 1

Simulation Time = 100000

Results

Average Assembling Queue Length = 1316.32

Maximum Assembling Queue Length = 2699

CDs Production Rate = 977/100000 à 844.128 CD/hour

Case Study

Assembling Queue input rate = -0.00983607

Assembling Queue output rate = 0.0724138

Required number of assembling machines

= 0.0724138/0.00983607 = 7.36

Conclusion

Applied Examples

PID System

Model 1 (Td=1)

Model 2 (Td=5)

Model 3 (Td=10)

Selector

Fuzzy PID

For large Td

Fuzzy PID

For small Td

Optimal P, I, D

For Td=5

Applied Examples

Identification Schematic

Process (Td=1,5,10)

Applied Examples

Control Loop Schematic

Applied Examples

Results

Main Topics

Background

Achievements

Related Tools

Related ToolsGPSS-World

Simulator supports only standard GPSS language

Standard

Very fast simulation

Based on command mode interface [not visual]

Supported programming language is very week

Simulation Output in text format, no graphs, no

direct analysis can be applied

Advantage

Disadvantage

Related ToolsSIMULINK

Represents any continuous time system using

Input/Output block diagram

Interact with Matlab functionality

Support Fuzzy and Neural systems

Does not support Discrete Event Systems

Support only floating point operations even

logical operation

Advantage

Disadvantage

Topics

Background

Achievements

Related Tools

Development Environment

Programming Environment: Microsoft Visual C++

version 6.0

Operating System: Windows 95,98,2000 or NT4

CPU: AMD K6-III 450 with 1 MB cash and 128 MB

Programming Language: C/C++

Aided tools: Matlab and SIMULINK version 4 and

5.1, GPCC/PC, GPCC world, QNET neural network

simulator, Neurosolulton simulator, FULDIC Fuzzy

simulator, and Matlab Fuzzy Component.

Software Specification

Source Code Size: above 50,000 line of code, 700file, 600 class, 150 exported function, 2,5 MB purecode, 309 MB project development size

Software Components: one executable file, 3 staticlibraries, 6 dynamic libraries

Tool Size: 3.9 MB for the complete released runningversion with libraries.

Minimum Hardware Requirements: 32 MB ram, 5MB disk space, Pentium 133 or any compatibleprocessor.

Conclusion

Both Continuous-Time and Discrete-Event systems can be simulated using Event-Driven Simulation with special treatment for Continuous-Time systems

Standard interface for libraries and components increase the ability for extension

Object-Oriented programming simplify the development, and produce modular code, easy to understand and easy to extend

Using internal interpreter gives the ability to define un-limited number of custom elements and functions. On another hand it slow down the simulation

The simulator succeeds to implement all proposed simulation languages, in spite they used different simulation techniques

Using internal interpreter increase the capabilities of GPSS language. It increases the ability to generate more statistics and to perform more complicated calculations

Future Work

This version supports non-interactive continues-time elements (input and outputs supposed to be buffered), and there is a need to support interactive continuous-time elements (electronic elements)

There is a need for a module that exports and imports standard simulation language file formats. Especially Matlab and Simulink file formats

Current version of the simulator does not support distributed simulation, for that, there is a need for modified technique that support this type

Simulation language can be extended by (Colored Petri Net, State Flow)

There is a need for native compiler for Power Matrix script

Thank You

Integrated Tools for the Analysis

and Design of Intelligent Control

System

Submitted by:

Eng. Mohamed Ali Ali Sobh

Supervised By:

Prof. Mohamed Abd El-hamid Sheirah

Ain Shams University, Faculty of Engineering

Computer and Systems Engineering Department

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