1. lecture structure and introduction - uni-saarland.de
TRANSCRIPT
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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
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0 50 100 150 200 250 300 350 400 450 5000
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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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al D
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pm
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Pro
ced
ure
in th
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Natu
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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