chapter1 comparison of control system

CHAPTER 1CHAPTER 1CHAPTER 1CHAPTER 1Comparison of Control System

RZAR/MKH/KEE/UPM/EEE4404

Outline

� Comparison between modern and classical

control and requirement of modern control

techniques.

Learning Outcome

� Able to differentiate between classical and

modern control and its importance

• Youtube

• intelligent home

– http://www.youtube.com/watch?v=48TFYpZQJpM&feature=related

• 3D Face - Face Reader - Access Control - Live

– http://www.youtube.com/watch?v=OAk_L_SesQQ&feature=related

• http://www.youtube.com/watch?v=cP035M_w82s&feature=related– This video shows the evolution of coordinated behavior of simulated robot soccer players. In the simulation,

each soccer player is controlled by a neural network. The neural networks are evolved using an evolutionary

algorithm, so generation after generation the strategy improves.

• http://www.youtube.com/watch?v=D21TF1WeNfM&feature=related– Artificial Intelligence is evolving 10 million times faster than the human brain!

• http://www.youtube.com/watch?v=IhVu2hxm07E&feature=related– Humanoid Talking Robot

• http://www.youtube.com/watch?v=_i-_1QdY2Zc&feature=related– A team of scientists funded by the US Defense Advanced Research Projects Agency (DARPA) have

implanted miniature neural and muscle stimulation systems into beetles to enable their flight to be remotely

controlled.

3

Motivation of Control Design

4

Objectives

5

Terminology of Control

Control System

CLASSICAL CONTROL MODERN CONTROL

Ex:

Bode Diagram

Nyquist Diagram

Root Locus

Compensating Networks

PID Controller

Nichols

Ex:

Optimal Control

Digital Control

System Identification

Adaptive Control

Robust Control

Fuzzy Control

Neural Control

• Transfer function representation of dynamic systems

�A dynamic system can have different transfer

functions.

�Transfer functions are independent of inputs and

initial conditions

Review of Classical

Control Theory

G(s)R(s)(i/p)

Y(s)(o/p) )(

)()(

sR

sYsG =

• Control system classification

� Open-loop control system

� Open-loop control is used when the model is accurate

and/or the performance requirement are not stringent.

� Example: washing machines, toaster, traffic lights

� Closed-loop control systems (feedback control

systems)

� Advantages: disturbance rejection, reduction of effects of

uncertain dynamic, improvement of system performance

(stability, transient and steady-state response)

Review of Classical

Control Theory (cont)

• Analysis tools:-

� Laplace transform

�Block diagram

• Commonly used control laws:-

�PID control

� Lead and lag compensation

Review of Classical


• Performance Evaluation:-

� Transient response analysis (impulse, step &

ramp response)

� 2nd-order systems (maximum overshoot,

settling time, rise time)

� Steady-state error analysis (ess)

� Stability analysis (Routh’s criterion, root

locus, Nyquist criterion)

Review of Classical


• Controller design tools

�Root locus

�Frequency response (bode diagrams, polar

plots)

�Nyquist criterion

�Gain and phase margin (bode diagram)

Review of Classical


• Multivariable plants (MIMO) � much more difficult to control than single-input-single-output (SISO) plants.

• Noise disturbances and errors in system modeling � need robust control laws

• Time-varying parameters � need adaptive control

Difficulties in Control

Difficulties in Control (cont)

• Nonlinearities (backlash, dead zone, friction in mechanical systems) � need nonlinear controls

• Data sampling in control implementation on computers � need digital controls

• Distributed parameters – which is an infinite-state (or infinite dimensional system)

Comparison of Classical and Modern Control Theories

Classical Control Theory Modern Control Theory

Dynamic system Linear time-invariant Linear time-invariantLinear time-varyingNonlinear

Input/outputs Single-input-single output (SISO) Multi-input-multi-output (MIMO)

Representations Transfer function State-space form

Domain of analysis Frequency domain 9s-domain) Mainly time domain and frequency domain

Mathematical tools Laplace transformComplex analysis

Matrix theoryLinear algebraSpace and operator theories

Feedback Output feedback Output feedbackState feedback

Typical control laws PID controlsLead/lag compensation

LQRPole assignment

Intelligent Control

• The term ‘INTELLIGENT CONTROL’ has a more general meaning and addresses more general control problems.

• That is, it may to systems which cannot be adequately described by a differential/ difference equations framework but require other mathematical models, as for example, discrete event system models.

Intelligent Control (cont)• More often, it treats control problems, where a

qualitative model is available and the control strategy is formulated and executed on the basis of a set of linguistic rules.

• Intelligent control may be used to denote a control technique that can be carried out using the “intelligent” of a human who are knowledgeable in the particular domain of control.

• If a human in the control loop can properly control a plant, then that system would be good candidate for intelligent control.

• Intelligent control seeks to achieve good

performance in machines, industrial processes,

consumer products, and other systems, by using

control approaches that, in a loose sense, tend

to mimic direct control by experienced humans.

• Information abstraction and knowledge-based

decision making that incorporate abstracted

information, are considered important in

intelligent control.

Intelligent Control (cont)

• Intelligent control techniques possess

capabilities of effectively dealing with incomplete

information concerning the plant and its

environment, and unexpected or unfamiliar

conditions.

• Many of these techniques can learn, adapt to

compensate for parameter changes and

disturbances, and are able to provide

satisfactory control even in incompletely-known

and unfamiliar situations.


• Overall, intelligent control technique can be applied to ordinary systems and more important to systems whose complexity defies conventional control methods.

• There are three basic approaches to intelligent control:

� fuzzy logic

� neural networks

� evolutionary computation : GA and GP


INTELLIGENT CONTROL

TECHNIQUES

• FUZZY LOGIC

� representing human knowledge in a specific domain of application and reasoning with that knowledge to make useful inferences of actions

• NEURAL NETWORKS

�massively connected networks that can be trained to represent complex nonlinear functions at a high level of accuracy.

�analogous to the neuron structure in a human brain

INTELLIGENT CONTROL

TECHNIQUES (cont)

• EVOLUTIONARY COMPUTATION�GENETIC ALGORITHMS (GA)

� optimization techniques that can evolve through procedures analogous to human evolution, where natural selection, crossover, and mutation are central

�GENETIC PROGRAMMING (GP)

� symbolic-based nonlinear optimization

� computationally simulated the evolution process by applying fitness-based selection and genetic operators to a population of parse trees of a given programming language.

INTELLIGENT CONTROL

TECHNIQUES (cont)

INTELLIGENT ADAPTIVE

CONTROL• Adaptive Control

�Is used to denote a class of control techniques where the parameters of the controller are changed (adapted) during control, utilizing observations on the plant (i.e. with sensory feedback), to compensate for parameter changes, other disturbances, and unknown factors of the plant.

• Intelligent Control + Adaptive Control (Intelligent Adaptive Control)

�the techniques that rely on intelligent control for proper operation of a plant, particularly in the presence of parameter changes and unknown disturbances.

INTELLIGENT ADAPTIVE

CONTROL (cont)

chapter1 comparison of control system

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