1. lecture structure and introduction - uni-saarland.de

Soft Control

(AT 3, RMA)

1. Lecture

Structure and Introduction

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Table of Contents

Computer Aided Methods in Automation Technology

• Expert Systems

Application: Fault Finding

• Fuzzy Systems

Application: Fuzzy Control (FC)

• Neural Networks (NN)

Application: Identification and Neural Control

• Genetic Algorithms (GA), Simulated Annealing (SA)

Application: Stochastic Optimization

• Basic Applications and Limitations of such Methods

Soft Control

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What is Soft Control?

Three classes of control methods

1. Conventional Control (Classical Control)

• PID controller

2. Modern Control

• State-Based Control

• Model Predictive Control

3. Soft Control (Intelligent Control)

• Fuzzy Control

• Neural Network

• Genetic Algorithms

Soft Control refers to those methods of control which use soft computing

and computational intelligence.

Soft Control = Intelligent Control = Knowledge-Based Scheme

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Problems of Conventional Control

• To design a conventional controller, a Macroscopic model of the controller

process is required

• The model may be based upon the empirical knowledge about the

dynamics of the controlled process

• This knowledge can be obtained from measurements on control and

controlled variables

• In practice, tuning of the control parameters is performed by the experts on

a running system

Example: Design of PID controller according to Ziegler and Nichols

Advantages:

Easy to use(few free parameters to configure, simple process model)

Robust

Problems:

Increased complexity of the requirements and constraints

Quality of control for complex controlled processes are often not sufficient

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Problems of Modern Control

• For the design of modern control, a microscopic model of the controlled

process is required.

• The model is determined through mathematical modeling

• Alternatively methods of identification can be used to ascertain the model

Example: Design of state-based control

Advantages:

Strong mathematical basis (stability, etc.)

High quality of control

Possible to include additional constraints

Problems:

Building a mathematical model of the controlled process is difficult and sometimes impossible

Detailed identification of process is often impossible or undesirable

Resulting controllers are complex and difficult to understand for the users

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Situation in the Industry

• Many conventional controllers at lower levels.

• Human as a controller at higher levels

• SCADA systems (Supervisory Control and Data Acquisition) provides

operators with all necessary information and access to the equipment

Advantages:

Operator can make intelligent decisions

Operator can learn by experience

Problems:

Quality of control depends on the experience of the operator

Interventions by the operator are subjective and often incomprehensive,

error-prone (especially under stress)

Under abnormal process conditions (alarm), the time delay in the decision-

making by the operator or the wrong decision by him can lead to disasters

(Chernobyl)

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Consequences

In modern complex systems, it is required that

• The operator performs the routine tasks that conventional

controllers are unable to solve

• The support of the decision-making process is provided, especially

in abnormal situations in which the operator is confronted often with

conflicting signals and objectives

In developing such systems

• Analytical process models are generally not available

• Objectives of the control scheme can often not be formulated

precisely

• In certain cases this results in formulation of conflicting goals

This requires intelligent controllers

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Artificial Intelligence (Künstliche Intelligenz)

• The biggest objective of Artificial intelligence is to emulate the

intelligent human behavior by means of computer programs.

• Symbolic and logic-based AI

Systems to solve problems

Systems for decision support

Knowledge-Based Systems

Formalisms for knowledge representation and AI programming languages

Knowledge acquisition and machine learning

• Intelligence through behavioral simulation

Turing Test

• Intelligence by symbol manipulation

Chinese Room

Philosophical discussion on the concepts of intelligence,

perception, awareness is not the aim of the lecture

Pragmatic approach

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Computational Intelligence (Soft Computing)

Artificial Intelligence

• Classical methods of artificial intelligence is based on the

processing of symbolic data

• Example: Expert systems

Computational Intelligence

• It refers to the methods that deal with numerical data

• Example: Fuzzy systems, Neuron Networks, Genetic algorithms

• Another denomination: Soft Computing

• Intelligent controllers are based on methods of soft computing, so

the name Soft Control

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Expert systems

• Core idea (Natural Model)

Human-like abstract thinking

• History

First expert systems began in 1970's (though faced the problem of high

computing expenses)

• Application in Automation Engineering

Today: Manifold industrial use higher levels of automation

• Examples

Expert systems to support process control

Expert systems for fault diagnosis

Training Systems (Simulators)

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Example of XPS: Diagnostic System in Process Control

Source: Polke 1994

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Fuzzy Systems

• Core Idea (Natural Model)

Dealing with fuzzy (non-crisp) knowledge

• History

In the mid-1960s Zadeh fuzzy logic

In the mid-1970s Mandani FuzzyControl

• Application in Automation Engineering

First industrial applications in the early 1980s

Fuzzy controller

• Examples

Drying processes

Gas heater

Fuzzy control of an inverted pendulum

Washing machine (AEG)

Fuzzy control of a hammer drill

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Example of Fuzzy: Control of a Hammer Drill

Task: Automatic control of optimum speed

and blow count with respect to

drill diameter and material hardness.

Solution: In total there are 20 IF-THEN rules for the determination of drill diameter and material hardness based on

four measured variables

Rule Nr. 11 as example:

IF Power=average AND Longitudinal acceleration=high AND

Transversal acceleration=high AND Longitudinal frequency=average

THEN Drill diameter=24mm

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Neural Networks

• Core Idea (Natural Model)

Connective approach for knowledge, storage and processing (neurons in the

brain)

• History

Beginning in the 1970s

Problems due to inadequate computing technology

New interest in the 1980s

• Application in Automation Engineering

Identification of complex processes

Control by inverse model

Prediction

• Examples

Identification of nonlinear systems

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Example of NN: Identification of a Two Tank System

h1

h2

qZu

L1

La

v12

va

)1(Zu -kq

)(ˆ1 kh)2(Zu -kq

)2(1 -kh

)1(1 -kh

0 50 100 150 200 250 300 350 400 450 500-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0 50 100 150 200 250 300 350 400 450 5000

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

0.02

(k)h

(k)h

1

1

ˆ

real

Model

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Genetic Algorithms

• Core Idea (Natural Model)

Stochastic Optimization (Evolution in Nature)

• History

Began in mid-1960s in Holland

• Application in Automation Engineering

From the mid-1990s for complex optimization problems (Offline)

• Examples

Optimizing control parameters especially with multiple degrees of freedom

(Fuzzy Controller)

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Interrelation Among the Methods

Fuzzy

Control

Neural

NetworksGenetic

Algorithms

Expert

systems

Adaptivity

Structure of Knowledge Processing

minimum

(not adaptive)

maximum

Unstructured

Structured

Populations Structure

Topology

of

Networks

Fuzzy

Rules

Control

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Classification into the Lecture

If you look at the systems presented so far, we can say that we

have looked at the intelligence from top-down :

• Expert Systems

(Abstract mathematical thinking)

are a further development of

• Fuzzy Systems

( "Natural" Fuzzy-Schlie sizes)

these could only develop on the basis

of the neural structures of the brain

• Neural Networks

(Learning and adaptation)

in the course of evolution arose from

much simpler structures by

• Genetic Algorithms

( "Survival of the fittest")

Tech

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Pro

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ure

in th

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ctu

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Natu

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t

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Summary

• The problems of industrial domain require the use of "smart"

controllers

• The research in the field of artificial intelligence and in particular the

Computational Intelligence offers a number of methods

• The ideas are quite old

• Found its application only since a some years ( mainly due to

computing power)

• The skepticism of the users has been significantly decreased

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Outline of Lecture

1. Introduction to Soft Control: Definition and Limitations, Basics of

“Smart" Systems

2. Knowledge Representation and Knowledge Processing (Symbolic AI)

Application: Expert Systems

3. Fuzzy Systems: Dealing with Fuzzy Knowledge

Application: Fuzzy Control

4. Connective Systems: Neural Networks

Usage: Identification and Neural Control

5. Genetic Algorithms: Stochastic Optimization

Application: Optimization

6. Summary & Literature

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Literature (Sources Used)

General Information about the AI: Comprehensive Reference Book for the Interested Students

• Götz, Güntzer (Hrsg.): Handbuch der künstlichen Intelligenz. OldenbourgVerlag, 2000.

Expert Systems: Application Oriented Interpretation for the Use in Control Engineering:

• Polke, M.: Prozeßleittechnik. Oldenbourg Verlag, 1994.

• Ahrens, W.; Scheurlen, H.-J.; Spohr, G.-U.: InformationsorientierteLeittechnik. Oldenbourg Verlag, 1997.

Methods of Computational Intelligence for the Automation

Engineering :

• Fatikow, S.: Neuro- und Fuzzy- Steuerungsansätze in Robotik und Automation. Vorlesungsskript, Karlsruhe, 1994.

• King R.E.: Computational Intelligence in Control Engineering. Marcel Dekker, 1999

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Objectives of the Course

To know what is the meaning of Soft Control

To know the AI and specially Computational Intelligence for

Automation Engineering related areas:

Expert systems

Fuzzy Systems

Neural Networks

Genetic Algorithms

To know the application, advantages, and dis-advantages of each

method

To understand and apply the design methods; specially for Soft

Control