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D R . T A R E K A . T U T U N J I
M E C H A C T R O N I C S Y S T E M D E S I G N
P H I L A D E L P H I A U N I V E R S I T Y
2 0 1 3
Control Systems: Overview
Outline
Basic Control Concepts
Control Techniques and Methods
Hardware Controllers
Control Systems
A control system is at the heart of mechatronic systems where electronics are used to control mechanical systems.
Open-Loop Control
[Ref] Kilian
Closed-Loop Control
[Ref] Kilian
Control Systems Classification
Control systems are classified by application.
Process control usually refers to an industrial process being electronically controlled for the purpose of maintaining a uniform correct output.
Motion control refers to a system wherein things move. A servomechanism is a feedback control system that provides remote control motion of some object, such as a robot arm or a radar antenna.
Process Control
[Ref] Kilian
Process Control Example
Motion Control
Motion Control Example: CNC Machine
[Ref] Kilian
Motion Control Example: Robotic System
[Ref] Kilian
Transfer Functions
Each component in the control system can be described mathematically by a transfer function (TF), where TF = output/input.
Transfer functions of individual components in a system can be mathematically combined to calculate overall system performance.
Transfer function includes transient and steady-state characteristics,
Transfer Functions
Example
[Ref] Kilian
General Block Diagram
[Ref.] Shetty
Basic Control Signals
[Ref.] Shetty
Basic Control Functions
[Ref.] Shetty
Performance Criteria
System Performance
Stability
Accuracy
Transient Response
Sensitivity
Stability
A stable system is one which produces a bounded, or finite, response when subjected to a bounded input
Stability conditions
A system is stable if the real part of all poles are < 0.
A system is marginally stable if real part of all poles are <= 0.
A system is unstable if the real part of any pole is positive.
Accuracy
Accuracy (or steady-state tracking error) is the error between input and output signals in the steady state for a system.
Three input signals can be used
Step
Ramp
Parabola
Accuracy
Transient Response
Transient response is the shape of a signal as it moves between two steady-state points.
It is quantified in terms of two parameters:
The damping ratio, z, pronounced zeta
The natural undamped frequency, wn.
Pole Locations
[Ref.] Shetty
Sensitivity
Sensitivity is the measure by which controlled signals are influenced by disturbances which include parameter variations within the plant and external signals such as noise.
Control Techniques
T A R E K A . T U T U N J I
Dr. Tarek A. Tutunji
Control Techniques / Strategies
Classical Control
Adaptive Control
Robust Control
Optimal Control
Intelligent Control
Dr. Tarek A. Tutunji
Classical Control
Classical control design are used for SISO systems.
Most popular concepts are:
Bode plots
Nyquist Stability
Root locus.
PID is widely used in feedback systems.
Classical Control: On-Off Control
This is the simplest method of control. The control action has three possible outputs: on; off; no change. This method is usually used for slow-acting operations (such as a refrigeration unit).
The advantage is its ease of design and low cost. However, it cannot vary the controlled variable with precision.
On-Off Control Example
Dr. Tarek A. Tutunji
Classical Control: PID
Proportional-Integral-Derivative (PID) is the most commonly used controller for SISO systems
dt
)t(deKdt)t(eK)t(eK)t(u DIp
Analog PID Implementation
[Ref] Kilian
Discrete PID Implementation
[Ref] Kilian
Classical Control: Root Locus
Classical Control: Compensators
Classical vs. Modern Control
In contrast to the frequency domain analysis of the classical control theory, modern control theory utilizes the time-domain state space representation.
A mathematical model of a physical system as a set of
input, output and state variables related by first-order differential equations.
The variables are expressed as vectors and the differential
and algebraic equations are written in matrix form.
The state space representation provides a convenient and compact way to model and analyze systems with multiple inputs and outputs.
Adaptive Control
Adaptive control involves modifying the control law used by a controller to cope with the fact that the parameters of the system being controlled are slowly time-varying or uncertain.
Such controllers use on-line identification of the
process parameters. For example, as an aircraft flies, its mass will slowly
decrease as a result of fuel consumption; we need a control law that adapts itself to such changing conditions.
Robust Control
Robust control is a branch of control theory that explicitly deals with uncertainty in its approach to controller design.
Robust control methods are designed to function
properly so long as uncertain parameters or disturbances are within some set.
The state-space methods were sometimes found to
lack robustness, prompting research to improve them. This was the start of the theory of Robust Control, which took shape in the 1980's and 1990's and is still active today.
Adaptive vs. Robust Control
Adaptive control does not need a priori information about the bounds on uncertainties or time-varying parameters.
Robust control guarantees that if the changes are within given bounds the control law need not be changed, while adaptive control is precisely concerned with control law changes.
Optimal Control
Optimal control is a set of differential equations describing the paths of the state and control variables that minimize a “cost function”
For example, the jet thrusts of a satellite needed to bring it to desired trajectory that consume the least amount of fuel.
Two optimal control design methods have been widely used in industrial applications, as it has been shown they can guarantee closed-loop stability. Model Predictive Control (MPC)
Linear-Quadratic-Gaussian control (LQG).
Dr. Tarek A. Tutunji
Intelligent Control
Intelligent Control is usually used when the mathematical model for the plant is unavailable or highly complex.
Intelligent controllers are also used when the system must make decisions (from several alternatives) based on input data from sensors
The most two commonly used intelligent controllers are
Artificial Neural Networks
Fuzzy Logic
Intelligent Control: Fuzzy
Fuzzy set theory provides mathematical tools for carrying out approximate reasoning processes when available information is uncertain, incomplete, imprecise, or vague.
Fuzzy logic controllers manage complex control problems through heuristics (IF … THEN) and mathematical models provided by fuzzy logic, rather than via mathematical models provided by differential equations.
This is particularly useful for controlling systems whose mathematical models are nonlinear or for which standard mathematical models are simply not available
Fuzzy Control
Intelligent Control: ANN
Artificial Neural networks (ANN) are nonlinear mathematical models that are used to mimic the biological neurons in the brain.
ANN are used as black box models to map unknown functions
ANN can be used for: Identification and Control
Dr. Tarek A. Tutunji
ANN: Single Neuron
y
w0
w1
wM
x1
x2
xM
f(net)
M
mmmwxfy
1
Neural Nets
TDL
TDL
Weights
Weights
Log
Function + Weights +
Log
Function
Plant
Output
Plant
Input
Net
Output
First Layer Second Layer
ANN: Identification and Control
Identification Control
ANN: Identification and Control
D R . T A R E K T U T U N J I
Hardware Controllers
Analog vs. Digital Control Systems
Analog Digital
Time variable Continuous Discrete
Time equations Differential equations Difference equations
Frequency transforms Laplace Z-Transform
Stability Poles on LHS Poles inside unit circle
Controller Hardware: Op-Amps Software: None
Hardware: Microcontroller Software: Program
Dr. Tarek A. Tutunji
Digital Control Block Diagram
Criteria for Choosing Controller
Price Size and Weight Number of Digital Inputs and Outputs Number of Analog Inputs and Outputs Speed Required Interrupt Required hardware Communication Interface Reliability Memory Programming Capability Software Support
Dr. Tarek A. Tutunji
Hardware Controllers
Microcontroller
PLCs
DSPs
FPGA
PC with DAQ
Dr. Tarek A. Tutunji
Microcontrollers
Microcontroller is a special type of small computer that can perform a specific job
Microcontrollers
The microcontroller is a computer-on-chip. It is an integrated circuit that contains microprocessor, memory, I/O ports and sometimes A/D converters. It can be programmed using several languages (such as Assembly or C/C++). It can be used in manufacturing lines, but requires additional hardware. Microcontrollers are mainly used in engineering products such as washing machines and air-conditioners.
Dr. Tarek A. Tutunji
Microcontrollers Companies
Microcontroller Market Share
Arduino
Arduino is an open-source electronics prototyping platform based on flexible, easy-to-use hardware and software.
The hardware consists of a simple open source hardware board designed around an 8-bit Atmel AVR microcontroller, though a new model has been designed around a 32-bit Atmel ARM
ARM
The ARM architecture describes a family of RISC-based computer processors designed and licensed by British company ARM Holdings.
As an IP core business, ARM Holdings itself does not manufacture its own electronic chips, but licenses its designs to other semiconductor manufacturers
PLCs
A Programmable Logic Controller (or PLC) is a specialized digital controller that can control machines and processes. it monitors inputs, makes decisions, and controls outputs in order to automate machines and processes
Programmable Logic Controller
PLC’s are a user-friendly, microprocessor-based, specialized computer that is used for process control. It contains input/output (I/O) modules for appropriate sensors/actuator interfaces. It is mainly used in automated manufacturing lines. The PLC is usually used for simple logic operations. It is considered reliable and easy to program (using ladder diagrams, instructions, or function blocks).
Dr. Tarek A. Tutunji
PLC Manufacturers
PLC vs. Microcontroller
Usually PLCs are used in an industrial environment, where as the microcontrollers are smaller and well suited for embedded situations.
PLCs are programmed with ready made blocks or programming elements, whereas in Microcontrollers a programming language must be used to write a programming code
PLC Advantages
They are highly reliable, fast and flexible.
They can handle severe conditions such as dust, humidity etc.
They can communicate with other controllers.
They are easy to program and troubleshoot.
They include display units.
Digital Signal Processors
Digital Signal Processing (DSP) is the arithmetic processing of discrete-time signals. A/D is needed for analog signals
Digital signal processors (DSP) are specialized microprocessors with
advanced architectures (such as multiple buses, parallel processing, hardware multipliers and fast sampling rate) that are designed to reduce the number of instructions and operations necessary for efficient processing.
DSP chips enable developers to implement complex algorithms and
perform computationally efficient and fast algorithms. DSP are preferred over microcontrollers when the need for complex and iterative
control algorithms is required.
The term “Digital Signal Controllers (DSC)” refers to the use of DSP as control elements
Dr. Tarek A. Tutunji
Commonly used DSP Operations
Convolution
Correlation
Fourier Transform
Power Spectrum
Digital Filtering
Dr. Tarek A. Tutunji
DSP Operations: Convolution Consider a system h(n) with input x(n) and output y(n)
h(n)
x(n) y(n)
X(Z)H(z)Y(Z)
h(n)*x(n)y(n)
Then,
The convolution of two signals, x(n) and h(n), is given by
-k
k)x(n)h(nh(n)*x(n)y(n)
•One signal is flipped and shifted with respect to the other . •Each element of one signal is multiplied by the corresponding element of the other. •All the elements are summed. •Correlation requires a lot of calculations.
Dr. Tarek A. Tutunji
DSP Architecture Features
Parallel Processing (Modified Harvard)
Deep Instructions Pipeline
Very Fast A/D
Hardware Multiplier
Barrel Shifter
RISC
Dr. Tarek A. Tutunji
Modified Harvard Architecture
A Harvard architecture employs separate program and data buses to access separate data and program memories.
A modified Harvard architecture.
DSP use multiple data buses (and multiple associated address buses) so that the processing of two signals can be done in parallel.
The address buses are also separate. This multiple bus arrangement increases speed since instructions and data can move in parallel, and execute simultaneously rather than sequentially.
Modified Harvard Architecture
DAGEN A
DAGEN B Memory
A Memory
B
ALU Multiplier
Shifter
Accumulators
Shifter Memory
C
DAGEN C
Instruction Pipelining
Up to six levels of pipelining are implemented.
DSP can execute instructions in parallel
Overall execution times are accelerated so that high
Hardware Multiplier
A 16- by 16-bit hardware multiplier multiplies and stores results in a 40-bit accumulator (8 guard bits) in a single instruction cycle.
Thus, multiply and accumulate operations can be performed in a single clock cycle in a DSP; conventional processors may require tens of cycles for this operation.
Shifters and RISC
Hardware shifters allow scaling, prevent overflows, and maintain required precision.
An on-chip hardware stack reduces interrupt response time and minimizes stack pointer manipulations.
DSP use reduced instruction sets tailored to digital signal processing operations. For example, the MACD command implements four operations in one instruction: multiplies two values moves data adds the product to a previous result transfers the result to an adjacent register.
Digital Signal Controllers Manufacturers
Texas Instruments.
TMS320C2000™ DSP Platform
Microchip.
dsPIC30F3010
Motorola
Custom made DSP Engines
Field Programmable Gate Arrays
The field-programmable gate array (FPGA) is a semiconductor device that can be programmed after manufacturing.
Instead of being restricted to any predetermined hardware function,
an FPGA allows you to program product features and functions, adapt to new standards, and reconfigure hardware for specific applications even after the product has been installed in the field—hence the name "field-programmable".
FPGAs can be used to implement any logical function that an application-specific integrated circuit (ASIC) could perform. One advantage is its ability to update the functionality after shipping.
FPGAs vs. Microcontrollers
FPGAs can perform concurrent operations while the microcontrollers’ operations are sequential. This makes FPGAs better suited for real-time applications such as executing
DSP algorithms.
FPGA are flexible, you can add subtract the functionality as
required. This can not be done in microcontroller. FPGAs are hard-wired and the random attack of alpha rays can not
destroy/corrupt the memory areas hence collapse the device functionality.
FPGA based development is longer while microcontrollers change
too often and there is lots re-work required to do in order to keep pace with changing technology. This is necessary to save the design from being obsolete.
Dr. Tarek A. Tutunji
FPGAs vs. Microcontrollers
The development time for microcontroller is shorter and that of FPGA
The microcontroller peripherals are readily available and tested by
the vendor. As for the FPGA, open source soft-peripherals are available, but still need to be embedded and tested.
Microcontroller are power efficient. Microcontroller are low-cost, much lower than FPGAs. This is
specially true for small applications and large quantities. Microcontrollers are available in easy to solder SOIC and QFP
package while FPGAs offer limited sources.
Dr. Tarek A. Tutunji
Personal Computers
Personal computers are used when extensive signal processing and in-depth analysis is required.
This will require Data Acquisition Cards (DAQs) to interface
the I/O power and signals between the PC and the environment.
Advantages include superior graphical and software
flexibility. However, the cost is high and, therefore, they are not suitable
for a large number of products Another disadvantage is the speed
Dr. Tarek A. Tutunji
PCs and DAQs
Summary
The selection of the controller is arguably the most important issue of the mecahtronics system
This choice can be divided into two parts:
1. Software/Firmware algorithm On-Off, PID, Adaptive, Robust, Optimal, and Intelligent
2. Hardware system Microcontroller, PLC, DSP, FPGA, and PC-DAQ
Dr. Tarek A. Tutunji