Table Of ContentAPPLICATION OF
ARTIFICIAL INTELLIGENCE
IN PROCESS CONTROL
Lecture notes ERASMUS intensive course
Edited by
L. BOULLART
A. KRIJGSMAN
and
R. A. VINGERHOEDS
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Preface.
This book is the result of a united effort of six European universities to create an overall
course on the application of artificial intelligence (AI) in process control. Starting from an
initiative of the Automat ic Control Laboratory of the University of Ghent, Belgium, an
intensive course was set up, aiming at university engineering students in their final study
year.
The proposal was followed by five other universities in the European Community (Uni-
versity College of Swansea, Delft University of Technology, Queen Mary and Westfield
College, Universitat Politecnica de Catalunya and Universidad Politecnica de Valencia),
bringing the total to 6 universities in 4 different countries. In October, 1989, the applica-
tion was submit ted to the European Community, to be carried out in the framework of the
ERASMUS program. This program stimulates education in Europe in important areas.
The idea of a joint course for the application of AI in process control fits perfectly in this
program.
AI technology is a relatively young discipline. Many research groups are working in
the domain, but especially on the application in process control where special problems
are encountered, no single university can offer a complete course on the subject. AI is,
however, very important for the European industry and its position in competition with
the U.S.A. and Japan. Therefore a combined effort is necessary to st imulate students to
step into the field.
The project was accepted and in September 1991 the first edition of the intensive
course was held in Ghent. More than 100 students from the participating universities
came together for two weeks, to get lectures from the specialists from those universities.
The results were excellent and, with some minor changes, new editions of the course are
held in September 1992 in Swansea and in September 1993 in Barcelona and Valencia.
The final goal is that the course should be held every year at another university in
Europe and the lecture notes should spread out over other European universities. In this
way not only can a good educational program be offered to the students , but also joint
research initiatives between the participating universities are stimulated.
The texts in this book serve as lecture notes for the course. They give a good overview
of the domain, the problems tha t can be encountered, the possible solutions using AI
techniques and how to successfully apply these techniques.
In part I, a general introduction to the field of A.I. is given. Thereby not only logic
and expert system techniques are discussed, but an in-depth overview of neural network
techniques is presented as well.
Part II focuses on control engineering and shows how control systems have evolved over
the years. From there an insight is given in where and how artificial intelligence techniques
can be used to improve control systerns.
ix
In part III, real-time issues are discussed. The importance of a good and solid com-
munication between the components of control systems is discussed and it is shown which
criteria are important to take into account. Furthermore, real-time expert systems are
introduced and their working is explained.
Part IV illustrates the use of artificial intelligence techniques for designing control
systems. It shows how the software environments for design tasks have evolved over the
years and where expert system techniques can give new impulses.
In part V, intelligent control itself is presented. In four chapters fuzzy (adaptive) control
and the use of neural networks for control are discussed.
Par t VI finally, focuses on supervisory control, monitoring and fault diagnosis and on
the use of expert system techniques in numerical optimization.
The book combines the ideas in research and education of six prominent universities.
It is our hope that it will bring a start to what can be seen as a standard for education in
this fascinating domain.
A c k n o w l e d g m e n t . The editors wish to thank the authors of the chapters in this book
for their enthusiastic cooperation. In particular, we wish to thank A.J. Krijgsman
and M.G. Rodd for their help in editing the book.
R.A. Vingerhoeds and L. Boullart
1
University of Ghent, Automatic Control Laboratory
editors
June 1992.
1
The editors can be contacted at:
Grotesteenweg Noord, 6, B-9052 Zwijnaarde, Belgium
X
Contributing authors.
University of Ghent
L. Boullart
R.A. Vingerhoeds
University College of Swansea
M.G. Rodd
C.P. Jobling
Queen Mary and Westfield College
J. Efstathiou
R. Vepa
Delft University of Technology
P.M. Bruijn
R. Jager
A.J. Krijgsman
H.B. Verbruggen
Universitat Politecnica de Catalunya
G. Cembrano
M. Tomds Saborido
G. Wells
Universidad Politecnica de Valencia
P. Albertos
A. Crespo Lorente
M. Martinez
F. Morant
J. Pico
Lawrence Livermore National Laboratories
G.J. Suski
XI
Road Map.
The first part of the course is a Basic Course in Artificial Intelligence for the graduate
student, who doesn't need a fundamental study in the field as such, but who nevertheless
needs a study which is thorough enough to be used as a fundament to other disciplines.
The target field primarily is process control, because of some applications chosen, but it
has been kept sufficiently loose from this in order to be more widely useful.
There are eight chapters:
A G e n t l e I n t r o d u c t i o n t o Artif icial Inte l l igence , with Appendix: ' A Rule Based Sys-
tem called "CLIPS '" . This chapter gives a general introduction in the field of A.I.
which covers also briefly topics from other chapters in order to keep consistency. One
part is more descriptive, while another deals with some topics more technically in
depth. The appendix gives a short overview of the production rule system 'CLIPS' ,
which is used throughout many examples in several chapters.
K n o w l e d g e R e p r e s e n t a t i o n by Logic . This chapter presents the fundamental under-
lying elements when logic is used for inference. It is useful for understanding Prolog
and production rules, for the basic resolving algorithms are being discussed.
O b j e c t - O r i e n t a t i o n and O b j e c t - O r i e n t e d P r o g r a m m i n g . The fundamental under-
lying elements of the Object Oriented ( 0 . 0 . ) formalism are presented. Some 0 . 0 . -
languages are compared in order to help the reader making the right decision if
necessary. Therefore this chapter is far more general than the A.I.-field alone.
E x p e r t S y s t e m Case S t u d y : T h e C h o c o l a t e Biscu i t Factory. This chapter presents
an educational case study of a rule based expert system. Its purpose is not to present
a typical application but merely to oifer an educational aid to the reader to under-
stand the basic principles of both production rules, reasoning methodologies and
expert systems.
U s i n g A. I . -Formal i sms in P r o g r a m m a b l e Logic Contro l lers . Here a typical appli-
cation in process control is presented, where both Prolog and production rules are
demonstrated in programmable logic controllers.
A n i n t r o d u c t i o n t o E x p e r t S y s t e m D e v e l o p m e n t . A complete survey of the activ-
ities involved in setting up an expert system project will be given. This involves
selecting appropriate knowledge acquisition techniques, knowledge representations,
the appropriate expert system shells, etc.
I n t r o d u c t i o n t o Fuzzy Logic and Fuzzy Se t s . Here fuzzy logic is introduced. It al-
lows the programmer to use fuzzy data, like the terrain is high (as opposed to the
terrain is 1345 m) .
An Introduction to Neural Networks. A complete survey on neural networks is pre-
sented. Starting from the biological neural networks, the link is made to artificial
neural networks, as they are used in AI.
This road map should enable the reader to make up his own choice in this basic course,
depending on his skills and future use.
A Gentle Introduction to
Artificial Intelligence.
Prof.Dr.ir Luc Boullart
Automatic Control Laboratory, University of Ghent.
1 Introduction.
'Intelligence' is commonly considered as the ability to collect knowledge and to reason with
this knowledge in order to solve certain class of problems. There are several reasons to try
to catch this intelligence in computers ('Artificial Intelligence': A.I.):
• to acquire a bet ter insight in the human reasoning process by designing a computer-
model 'in his own image';
• to ease the use of computers, by giving them a more 'human ' face;
• to create the possibility to solve very complex problems, which could not be solved
by standard programming, or could only by the expense of huge efforts;
In the early stage of computing (early 50's), it was completely unthinkable computers
would ever do something else than computing ballistic curves. The problem undoubtedly
lies in the terminology itself, where 'computer ' always associates with 'counting' and 'cal-
culating' , although the machines in principle could manage all kind of symbols. Even John
von Neumann has argued in his last publications that computers never would reach any
stage of intelligence.
The first applications of A.I. existed already in the 50's and the early 60's especially
in the form of chess and checkers programs. The purpose of the designers thereby was
to get an insight in the essential reasoning process of famous chess players: there was a
general presumption tha t some kind of general principle lied on the base of each intelligent
behavior. Therefore, chess programs were for a long t ime considered as a kind of ult imate
benchmark for A.I.. To-day, such machines play at a sufficient level to win in 99% of
the cases. On the other hand, specialists have realized that the intelligent behavior of
such programs only illustrate a few aspects of the reasoning process: a good chess player
is only (not less, but also not more) than a good chess player. This change in at t i tude
was very characteristic, and meant a swap from research in general reasoning principles
to the recognition of more specific knowledge (facts, experimental knowledge), and its
impact in specific domains. This swing in mentality in the scientific research was triggered
by experiments in specific projects, which demonstrated that it was possible to use large
amounts of knowledge in an 'intelligent' way, leading to results which could never have
been reached by human action alone.
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2 Definition of Artificial Intelligence.
As a relatively young science, there is no strict definition of A.I.. A definition often referred
to, especially in scientific research, is to regard A.I. as a collection of techniques to handle
knowledge in such way as to obtain new results and inferences which are not explicitly
('imperatively') programmed. Using A.I., of course involves a strong need for a number of
tools and methods. These methods and techniques are in many cases transferable to other
application areas. A much broader definition of A.I. therefore contains also all applications
which employ A.I.-techniques, whether or not they enable the inference of new knowledge.
Some definitions by prominent A.I.-specialists are the following.
• "Artificial intelligence is the science of making machines do things that require intel-
ligence if done by men" (Marvin Minsky, MIT).
• "The goals of the fields of Artificial Inteligence can be defined as . <atempting>
to make computers more useful <and> to understand the principles that make intel-
ligence possible" (Patrick Winston, MIT).
• "The field of artificial intelligence has as its main tenet that there are indeed common
processes that underline thinking and perceiving, and furthermore that these processes
can be understood and studied scientifically ... In addition, it is completely unimpor-
tant to the theory of A.I. who is doing the thinking or perceiving: man or computer.
This is an implementation detail" (Nils Nilsson, Stanford University).
• "A.I. research is that part of computer science that investigates symbolic, non algo-
rithmic reasoning processes and the representation of symbolic knowledge for use in
machine inteligence" (Edward Feigenbaum, Stanford University).
A.I. has undoubtedly a growing impact and enjoys an increasing interest from many
(especially industrial) users. Computers are performing, all things well considered, rather
good, even when processing large amounts of data (knowledge). On the other hand A.I.
is not a magic toolbox to solve all problems, but the advance towards more complex ones
which could not have been readily solved by 'normal ' imperative programming methods,
is quite noticeable. The usefulness of the techniques in well defined application areas, has
pulled A.I. out of its ivory tower.
One of the key questions which may arise is: when a system will it be intelligent?
Although chess programs were considered long t ime as a benchmarch, there exist a so called
Turing test (Alan Turing). This test mainly consists in a scene whereby a person by means
of a terminal interrogates both a computer and another (invisible) person. Turing states a
system is intelligent when the interrogator cannot distinguish between the computer and
the physical person. Until now, this experiment has only succeeded in some well defined
and strictly limited situations. This is caused by the fact the computer cannot yet interpret
subtle nuances, read between lines, nor construct a 'general ' knowledge in many different
fields. More useful tests, e.g. exist in the execution of extensive realistic test cases in the
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specific domain of the system under development. In this way, a typical medical diagnosis
expert system (PUFF), scored as follows:
• 96% agreement between the system and the diagnosis of the specialist whose knowl-
edge has been transferred;
• 89% agreement of the system with other independent specialists;
• 92% agreement among independent specialists.
3 Application areas of A.I..
Although the application field of A.I. is very wide and certainly not fully explored, research
has given noticeable results in the form of useful tools and applications. These applications
can be divided in a number of specific domains.
3.1 Expert Systems.
An expert system applies A.I.-techniques to an amount of knowledge concerning a well
defined area, and stored in a data base. The ultimate purpose is to mimic and surpass the
human expert. The efficiency of expert systems at this time is mainly determinated by the
quality and the quantity of the gathered knowledge. A number of special techniques exist
of course to deal with this knowledge on a intelligent basis, but there are no real generic
reasoning techniques. Nevertheless, results are sometimes quite amazing.
3.2 Systems for natural language expression.
The purpose here is to engage a communication with computer systems in a more 'natural
language' approach instead of a traditional procedural language. Thereby both written
and spoken text will be used. Written text is processed via keyboards, printers, video
systems a.o., while speech is processed with special hardware systems for recognition and
synthesis. This application area furthermore splits up into two branches:
• natural language regarded as pure text manipulation (syntactic);
• natural language with recognition and production of a meaningful content (semantic);
Possible applications could be:
• interfacing to data bases, software systems, expert systems, robots, etc.;
• translations of written text from one natural language into another;
• document processing: 'understanding' written documents in order to summarize,
indicate important elements, interrogate, etc. . . .
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