Table Of ContentIFAC SYMPOSIA SERIES
Janos Gertler, Editor-in-Chief, George Mason University, School of Information Technology and Engineering,
Fairfax, VA 22030-4444, USA
DHURJATI & STEPHANOPOULOS: On-line Fault Detection and Supervision in the Chemical
Process Industries (1993,No.l)
BALCHEN et al: Dynamics and Control of Chemical Reactors, Distillation Columns and Batch
Processes (1993,No.2)
OLLERO & CAMACHO: Intelligent Components and Instruments for Control Applications
(1993,No3)
ZAREMBA: Infonnation Control Problems in Manufacturing Technology (1993,No.4)
STASSEN: Analysis, Design and Evaluation of Man-Machine Systems (1993, No.5)
VERBRUGGEN & RODD: Artificial Intelligence in Real-Time Control ( 1993,No.6)
FUESS: Nonlinear Control Systems Design ( 1993, No.7)
DUGARD, M'SAAD & LANDAU: Adaptive Systems in Control and Signal Processing ( 1993, No.8)
TU XUYAN: Modelling and Control of National Economies ( 1993,No.9)
LIU, CHEN & ZHENG: Large Scale Systems: Theory and Applications ( 1993,No.JO)
GU YAN & CHEN ZHEN-YU: Automation in Mining, Mineral and Metal Processing (1993,No.J1)
DEBRA & GOITZEIN: Automatic Control in Aerospace (1993, No.12)
KOPACEK & ALBERTOS: Low Cost Automation (1993,No.13)
HARVEY & EMSPAK: Automated Systems Based on Human Skill (and Intelligence) (1993,No.14)
BARKER: Computer Aided Design in Control Systems (1992,No.J)
KHEIR et al: Advances in Control Education (1992,No.2)
BANYASZ & KEVICZKY: Identification .and $ystem Parameter Estimation ( 1992,No3)
LEVIS & STEPHANOU: Distributed"Intelligerice SysteJris. (1992,No.4)
FRANKE & KRAUS: Design Methods of Control Systems (1992,No.5)
.
ISERMANN & FREYERMUTH: Fault Detection, Supervision and Safety for Technical Processes
(1992,No.6)
TROCH et al: Robot Control (1992, No.7)
NAJIM & DUFOUR: Advanced Control of Chemical Processes ( 1992,No.8)
WELFONDER, LAUSTERER & WEBER: Control of Power Plants and Power Systems ( 1992,No.9)
KARIM & STEPHANOPOUI.DS: Modeling and Control of Biotechnical Processes (1992,No.JO)
FREY: Safety of Computer Control Systems 1992
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IFAC SYMPOSIUM ON ARTIFICIAL INTELLIGENCE IN
REAL-TIME CON1ROL 1992
Sponsored by
International Federation of Automatic Control (IFAC)
Technical Committees on
- Computers(COMPUT)
- Manufacturing Technology(MAN.TECH)
- Applications (APCOM)
- Social Effects of Automation (SOC.EFF)
Co-sponsored by
International Federation for lnfonnation Processing(IFIP)
International Association for Mathematics and Computers in Simulation (IMACS)
Organized by
Royal Institution of Engineers in the Netherlands
International Programme Committee
M.G. Rodd(UK) (Chairman)
K.J. AstrOm(S)
L. Boullart(B)
P. Bome(F)
H.J. Efstahiou(UK)
S. Franzen(S)
A. Halme (SF)
C.C. Hang (SGP)
C.J. Harris (UK)
G. Johannsen (D)
I.G. Kalaikov(BG)
V. Krebs (D)
R. Lauber (D)
National Organizing Committee
H.B. Verbruggen (Chainnan)
J.M. van der Kamp
L. Boullart
P.M. Bruijn
R.B.M. Jaspers
A.J. Krijgsman
Th. .Kristel
H.T. Li (PRC)
I.M. MacLeod(SA)
L. Motus(ESTONIA)
S. Narita(J)
Y.J. Pao (USA)
L. Pun(F)
A.G. Schmidt (D)
S.O. Su(PRC)
G.J. Suski (USA)
S.G. Tzafestas(GR)
T. Vamos(H)
H.B. Verbruggen (NL)
E.A. Woods (N)
Real-time Environments for Intelligent Control
Of the plenary papers presented, the following two papers have been published in Control EngiMering Practice,
Volume 1, Number 2 (Pergamon Press).
Autonomous controllers
Astrom K.J. (S)
Toward intelligent control of mechanical processes
Isermann R. (D)
In this volume the following two plenary papers have been inserted:
Knowledge-based control: selecting the right tool for the job
Leitch R. (UK)
The functional-link net approach to the learning of real-time optimal control
Pao Y.H.
The programme included 24 technical
(USA)
sessions, with three sessions taking place in parallel. The following regular
and invited sessions were scheduled:
Neural Network Schemes Fault Detection and Fault Diagnosis I
Knowledge Elicitation and Acquisition Neural Nets and Simulation for Control
Applications of Fuzzy Control Qualitative Reasoning
Temporal Reasoning Applications in Control and Measurement
Analysis and Design of Intelligent Controllers Applications in Biotechnology II
Applications of Neural Nets Applications in Process Control I
Process Monitoring and Supervision Fault Detection and Fault Diagnosis II
Fuzzy Control Genetic Algorithms and Learning
Learning Control Schemes Real-Time Environments for Intelligent Control
Direct and Supervisory Knowledge-based Control Fault Detection and Fault Diagnosis ID
New Paradigms for Real-Time Control Applications in Process Control II
Applications in Biotechnology I Development of Real-Time Al-Systems
Thirteen papers were selected for Volume 1, Number 2 of Control Engifll!ering Practice.
A selection of the remaining papers duly presented at the meeting was made by the editors for inclusion in the
Proceedings.
About 35% of the papers presented reported on practical applications, 30% dealt with theoretical aspects and 35%
had a mixed content of application-oriented and theoretical subjects.
Many of the professional engineers working in industry have the feeling that the gap between theory and practice in
applying control and systems theory is widening rather than narrowing despite so many years spent on developing
control algorithms. Much of this theory is heavily based on linear systems theory and on extensive mathematical
models.
In practice, however, many systems are partly unknown and highly nonlinear, and an increasing number of people,
confronted with real-life problems, feel that the elegant road paved by linear systems theory is leading a number of
applications into a dead end. Instead of a mathematical description, an alternative could be a behavioural
description based on qualitative expressions and on the experience of those actually working with the process.
This Symposium showed clearly that there are alternative possibilities for control based on artificial intelligence
techniques, and in many ways this Symposium has provided a large-scale breakthrough for artificial intelligence
techniques in control engineering.
In general, and according to the statements of many participants, this Symposium can be considered a very
successful event which showed the importance of this new, developing area for control engineering.
The next event on the topic of Al in real-time control is planned for Valencia in 1994.
Prof.Ir. H.B. Verbruggen
Prof. M.G. Rodd
Copyright © IFAC Artificial Intelligence in Real-Time
Control, Delft, The Netherlands, 1992 PLENARY PAPERS
KNOWLEDGE BASED CONTROL:
SELECTING THE RIGHT TOOL FOR THE JOB
R. Leitch
Intelligent Automation Laboratory, Department ofElectrical and Electronic Engineering,
Heriot-Wall University, EdinburghEHJ 2HT, UK
Absract. We propose a classification of system models in terms of their knowledge classes and
characteristics, and relate these to existing approaches to the use of AI methods in Control. Such
an classification is a necessary precursor to developing a methodological approach to identifying
the most appropriate technique (tool) for a given generic class of applications (job).
Keywords. Systems Modelling, Qualitative Modelling, Expert Control, Model Based Control.
Specification Methodology.
INTRODUCTION APPROPRIATE MODELLING:
- that's the secret
Approaches to the utilisation of Artificial
Intelligence (Al) methods for extending the range
of automation continues to expand at an ever
increasing pace. Each technique results in a
number of new potential solutions. The result is
that the practising Control Engineer is bewildered
by the seemingly endless procession of techniques
each offering some prospects of solving a given
automation problem. But how does he choose
what's best? Does he go for the latest Advanced
Control method based on evermore sophisticated
mathematics or is he seduced by the promise of
Intelligent Systems using simple qualitative
methods to produce flexible and effective systems.
Or does he need both! These days the word 'model' is a heavily
overworked term. In its most general form it can
be used to mean any description of an entity.
However what is crucial is to clearly understand
the role of the model. Within engineering, models
have long been used to predict the temporal
evolution of the attributes of a physical system,
often now called the behaviour of the system.
However, recently, mainly stemming from the AI
community, modelling techniques for reasoning
about the topological properties or spatial position
of objects and methods for representing and
reasoning about the function qf systems have also
been developed. Although these latter
developments are interesting they have not yet
impacted on Control Engineering. We will,
therefore, restrict the subsequent discussion to
models for the purpose of predicting behaviour,
sometimes called behavioural models and
descriptions.
At present the Control community is not addressing
this crucial problem of determining the most
appropriate approach dependent upon the nature of
the automation tas k and the characteristics of the
system that is to be automated. Such a
methodological approach is essential if effective
use of both AI-based systems and 'conventional'
control methods is to be established. A corollary
to this is that we need to stop looking for a
universally best approach, and put much more
effort into understanding the assumptions and
therefore the limitations of the various techniques.
Only in this way can we select the right tool for the
job. Further, we must also consider the purpose (or
task ) for which the model is being developed. For
example, it has long been recognised that models
for open-loop and feedback control require
different amounts of detail to achieve a similar
performance. Now, with Control Engineering
expanding its horizons to include other tasks, e.g.
fault diagnosis, process monitoring, planning,
training, etc., we must carefully consider the
1
relationship between tas k and model requirements.
There will be no one model that is best suited to all
task s. This 'no best model' is fundamental to
Engineering, whereas in Science, where the task of
modelling is almost exclusively analytic - to
describe the physical world as accurately as
possible - the notion of best model may be valid.
In Engineering, concerned with synthesis as well as
analysis:
a model is correct if its satisfies its purpose.
Also, synthesis is usually expressed as a set of
performance specifications for the system. So,
even a best or optimal model can be difficult, and
sometimes impossible to obtain. We are normally
faced with a trade-off between some of the
For example, accuracy of
specifications.
predictions and generality of the model can
sometimes be conflicting requirements. Further,
AI based approaches emphasise the need for
'understandability' or perspicuity of models as an
important specification requirement. In fact, many
of the existing AI approaches and those under
development, explicitly address this issue of
enhancing 'perspicuity', sometimes at the notional
expense of accuracy, so that the system can be
more easily modified or extended. Therefore, in
developing a model we have to consider the role
(behavioural prediction), the task (control,
diagnosis, training, etc.) and the performance
specifications (accuracy, flexibility, generality,
verifiability, perspicuity - and honesty).
Honesty! What has honesty got to do with
modelling?
Well,
what
has
been
under
development within AI based approaches are
techniques that allow the modeller to represent the
available knowledge in a model at the degree of
precision and certainty that is confidently known -
no less and no more. That is, if the knowledge is
uncertain, and perhaps even incomplete, we should
provide representation and reasoning mechanisms
to explicitly support such knowledge, and not
require the modeller to make 'guesses' or estimates
that he may not believe in for the model to become
tractable. This last insight is particularly important
and has resulted in an enormous interest in using
AI techniques to develop altemative(qualitative or
non-numeric) modelling approaches to cope with
such issues (Weld,1989; Davis,1990; Leitch,1990)
a whole plethora of techniques based on a wide
range of assumptions and normally developed for
specific task s. It is important that we now try to
understand the relationships between such models
and most crucially identify a methodology for
selecting the most appropriate technique for a
2
given
purpose,
task,
specification
characteristics of the available knowledge.
and
APPROACHES TO MODELLING
The preceeding section argued that the approaches
to developing models has expanded rapidly over
the last few years. Unfortunately, most of these
techniques have been developed in isolation, and
partial ignorance, of other approaches and so very
little understanding or taxonomic knowledge of the
various approaches exists. This section mak es an
attempt to classify the existing assumptions behind
the various approaches so that we can begin to
understand the relationship between them. We
first classify models into model classes and model
types and then identify a number of dimensions for
each. The former is used to classify the important
assumptions that relate to the purpose of the model,
whereas, the latter relates to the characteristics of
the available knowledge.
Model Classes
This class of models reflects very fundamental
assumptions about the model that are closely
related to the purpose of the model. We identify
three class dimensions:
knowledge source,
knowledge level and knowledge orientation. In
fact, combinations of these dimensions lead to
completely different approaches and research
topics.
By knowledge source we mean where the
knowledge that is used to build the model comes
from. Two major sources of such knowledge have
been identified (Leitch,1989) as empirical and
theoretical. Empirical knowledge relates to that
which is obtained directly from first hand
experience. It attempts to capture knowledge that
has been induced from direct observation of a
particular system.
As such it can be highly
effective but is limited in its generality. Empirical
knowledge has traditionally been omitted from
control systems design, sometimes resulting in
reduced performance, and hence requiring
subsequent empirical tuning.
However, the
development of Expert Systems techniques has
brought such knowledge to the fore and
emphasised its importance and, more recently, its
limitations.
On the other hand, theoretical
knowledge, that is knowledge of scientific laws
and principles, has long been the basis of control
system design.
However, the use of such
knowledge has, until fairly recently, been almost
exclusively restricted to numerical descriptions
usually in the form of differential or difference
equations. And, as discussed in the motivation,
often there does not exist adequate knowledge to
make use of the powerful methods associated with
real-valued differential equations. The Artificial
Intelligence community has, however, developed
techniques that allow theoretical knowledge to be
represented qualitatively and used to generate
qualitative descriptions of the system's behaviour
(Weld,1989; Leitchl990). Theoretical knowledge
is, of course, general and is transferable from one
application to another and, in fact, removes much
of the knowledge acquisition problem associated
with empirical k nowledge. However, it can also be
inefficient, and its very generality may mean that it
is less effective. Clearly, theoretical and empirical
knowledge are complementary; the best solution is
obtained by a symbiotic combination of the two.
However, such combinations are by necessity
specific to a given application (Leitch,1989) and
care has to be taken to ensure that performance is
indeed improved.
The second class dimension determines the subject
of the know ledge. In Control Engineering terms
we can have two options. We can represent the
knowledge of the process itself, i.e. model-based
approaches, or of the control algorithm, we term
this object-level knowledge. Alternatively, we
may choose to represent knowledge about the
control design methods so that they can be
modified on-line. We term this meta-level
knowledge, as it reflects knowledge about the
knowledge used to control rather than the
modelling knowledge itself. Both approaches are
actively being developed, both with AI-based
techniques and 'traditional' control methods. For
example, expert or intelligent control
(Astrom,1986) can be described as a meta-level
approach, usually with empirical knowledge at the
meta-level and a conventional numerical
controller(s) at the object level. In contrast, Fuzzy
Logic Controllers (Mamdani,1976) can be regarded
as object-level empirical knowledge (with
uncertainty). Similarly, in the case of conventional
control techniques, examples of object-level
control are classical three term controllers or
indeed an LQG derived state feedback control
system. Adaptive control systems, are a common
form of meta-level control as the performance of
the system is monitored on-line, usually in the
form of some performance index, and used to
modify the (design) of the object level controller.
This clear separation of meta and object level
knowledge allows different techniques (models) to
be used at each level, thereby greatly expanding
the range of applicability of the control techniques.
This distinction is fundamental in control
applications, however, it is also valid within other
3
domains, e.g. diagnosis. We term this class
dimension the knowledge-level.
A further distinction has to be made, and that is
whether the knowledge represents an explicit
model of the physical world to be reasoned about
or whether it represents our procedures for
controlling or diagnosing the world. In the latter
case the model would be termed implicit. Explicit
models relate system inputs to outputs in the same
way as the real system. They can, therefore, have
a causal interpretation (Iwasak i,1986) associated
with the structure of the representation.
Conversely, implicit models effectively relate
outputs (symptoms in the case of diagnosis) to
inputs and are inherently acausal.
object
Figure!. Model Classes
In the former case, explicit models are currently
being utilised as the basis for model-based
reasoning, in particular for diagnosis, but control
and training are equally important tasks. Explicit
models are usually from a theoretical source,
however, they need not be. In fact, much of causal
modelling (Console,1989)takes its knowledge from
empirical sources. Implicit models can also be
obtained from both sources of knowledge.
Conventional control algorithms are derived from
theoretical models using some design procedure,
whereas Fuzzy Logic Controllers utilise implicit
models based on empirical knowledge at the object
level. From Figure I , we can see that various
approaches to control can be identified by
appropriate combinations of the above dimensions.
We believe that these class-dimensions provide an
important insight into the relationships between
many of the fundamental techniques currently
under development.
Model Characteristics
information about causality; that must be obtained
from another source (Leitch,1987) This lack of
directionality makes the representation very
general but can also make the reasoning or
inference mechanism inefficient. Conversely, if
the available knowledge contains a strong element
of directionality between the variables then a
procedural language will be more effective.
However, the directionality may be very specific to
a given application or situation, and hence
procedural representations tend to be highly
specialised. The trend has been to make
representations more and more declarative to
increase generality and to cope with the resultant
loss in efficiency by more computational power.
Whilst model class is determined by the purpose
and task of the model, the properties of the
available knowledge used to model the process
determine the characteristics of the model that can
be utilised. These characteristics are used to form
dimensions along which models can be classified
and hence used to identify the most appropriate
model. It is in this area that AI is having the most
significant impact. In fact, the issues here are
exactly those that underpin knowledge
representation issues within Al, and are, therefore,
intrinsically fundamental to AI itself. Figure 2
illustrates the characteristic dimensions. We
identify five dimensions that represent the
principal assumptions for modelling and reasoning
about the physical world; in other domains other
characteristics may be more important. The third characteristic dimension concerns
whether the system is considered to be continuous
In the continuous case, the
or discontinuous.
system can only evolve through adjacent states
whereas in the discontinuous case any state can
follow a previous one, e.g. finite state machine.
This leads to different techniques for generating
the behaviour of the system. Continuity is clearly
an important assumption in dynamic systems.
However discontinuous dynamic systems can also
be important.
A fundamental choice is whether to represent the
dynamic evolution of the system or not. Until
fairly recently most AI-based representation
schemes were based on static models of the system
assumed to be in equilibrium. Such models can,
indeed, be useful especially in steady-state fault
diagnosis. However, in (model-based) control and
in diagnosing faults during the transient behaviour
of a system, dynamic models are essential.
Dynamic models require the representation of state
and memory to reflect the energy storage, and
hence delay, that occurs in the physical world.
This is often confused, at least in AI circles, with
temporal reasoning that reasons about the ordering
of events in time. Static models can still have
time-dependent variables, and even time-varying
parameters, without being dynamic. Hence many
temporal reasoning applications are based on static
models. The choice of static or dynamic models
fundamentally effects the representation language.
In the former, algebraic equations will suffice
whereas in the latter differential primitives are
required. An area of intense activity, now concerning both
Control Engineering and AI researchers is
Qualitative Modelling (Weld,1989, Leitch 1990).
Although this will not be specifically discussed in
this paper, it forms one of the main characteristic
dimensions of models. This dimension concerns a
spectrum of representations from purely
quantitative models at one end and very weak
qualitative representation at the other. In fact,
exploring novel representation techniques, e.g.
order-of-magnitudes and fuzzy sets has been a
major pre-occupation of qualitative reasoning
research (Shen,1992a). The goal is to utilise a
representation that 'honestly' captures the available
knowledge whilst satisfying the performance
specifications.
One of the early insights to stem from AI work is
the distinction between declarative and procedural
representations.
Declarative representations
describe relationships between variables or
attributes of the physical world. They do not imply
a directionality in the relationship, only that a set
of variables are related by the description provided.
For example, Ohm's law states that the current
through and voltage across a resistor can be related
by an empirical constant (given real-valued
descriptions of the current and the voltage, see Finally, the fifth characteristic dimension is
whether the k nowledge of the model is uncertain or
exact. Not to be confused with qualitative; a
model can be qualitative and exact, and even
honest! However, if the knowledge is uncertain
then the representation should include some way of
representing this. Two main forms of uncertainty
have been recognised.
The first, probability
theory, concerns the situation when exact
(deterministic) knowledge is not available and
estimates based on the frequency of occurrence,
represented by a probability density function, are
used.
It is essentially historically or
later) called the resistance. It does not contain any
4
