Table Of ContentAdvances in Industrial Control
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Other titles published in this Series:
Microcomputer-Based Adaptive Control Applied to Thyristor-Driven D-C Motors
Ulrich Keuchel and Richard M. Stephan
Expert Aided Control System Design
Colin Tebbutt
Modeling and Advanced Control for Process Industries, Applications to Paper
Making Processes
Ming Rao, Qijun Xia and Yiquan Ying
Modelling and Simulation ofP ower Generation Plants
A.W. Ordys, A.W. Pike, M.A. Johnson, R.M. Katebi and M.J. Grimble
Model Predictive Control in the Process Industry
E.F. Camacho and C. Bordons
RoAerospace Control Design: A VSTOL Flight Application
R.A.Hyde
Neural Network Engineering in Dynamic Control Systems
Edited by Kenneth Hunt, George Irwin and Kevin Warwick
Neuro-Control and its Applications
Sigeru Omatu, Marzuki Khalid and Rubiyah Yusof
Energy Efficient Train Control
P.G. Howlett and P.J. Pudney
Hierarchical Power Systems Control: Its Value in a Changing Industry
Marija D. IIic and Shell Liu
System Identification and Robust Control
Steen T0ffner-Clausen
Genetic Algorithms for Control and Signal Processing
K.F. Man, K.S. Tang, S. Kwong and W.A. Halang
Advanced Control ofS olar Plants
E.F. Camacho, M. Berenguel and F.R. Rubio
Control ofM odern Integrated Power Systems
E. Mariani and S.S. Murthy
Advanced Load Dispatch for Power Systems: Principles, Practices and Economies
E. Mariani and S.S. Murthy
Supervision and Control for Industrial Processes
Bjorn Sohlberg
Modelling and Identification in Robotics
KrzysztofKozlowski
Pietro Carlo Cacciabue
Modelling and
Simulation of
Human Behaviour
in System Control
With 113 Figures
, Springer
Dr Pietro Carlo Cacciabue
European Commission Joint Research Centre
Institute for Systems, Information and Safety
21020 ISPRA
Varese
Italy
British Library Cataloguing in Publication Data
Cacciabue. Pietro. C.
Modelling and simulation of human behaviour in system
control. - (Advances in industrial control)
l.Human behaviour - Computer simulation 2.Control theory
3.Human-machine systems
I.Title
629.9
Library of Congress Cataloging-in-Publication Data
Cacciabue. Pietro. C.
Modelling and simulation ofhurnan behaviour in system control 1
Pietro Carlo Cacciabue.
p. cm. -- (Advances in industrial control)
ISBN-13: 978-1-4471-1569-4 e-ISBN-13: 978-1-4471-1567-0
DOl: 10.1007/978-1-4471-1567-0
1. Automat control--Mathematical models. 2. Automatic control
-Computer simulation. 3. Human-machine systems--Mathematical
models. 4. Human-machine systems--Computer simulation. I. Title.
II. Series.
11213.C2 1998 97-51768
629.8--dc21 CIP
Apart from any fair dealing for the purposes of research or private study. or criticism or review. as
permitted under the Copyright. Designs and Patents Act 1988. this publication may only be reproduced,
stored or transmitted, in any form or by any means, with the prior permission in writing of the
publishers. or in the case of reprographic reproduction in accordance with the terms oflicences issued by
the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent
to the publishers.
© Springer-Verlag London Limited 1998
Softcover reprint of the hardcover 15t edition 1998
The use of registered names, trademarks, etc. in this publication does not imply. even in the absence of a
specific statement, that such names are exempt from the relevant laws and regulations and therefore free
for general use.
The publisher makes no representation. express or implied, with regard to the accuracy of the
information contained in this book and cannot accept any legal responsibility or liability for any errors or
omissions that may be made.
Cover desip by Uno Parisi
Typesetting: Camera ready by author
69/3830-543210 Printed on acid-free paper
Advances in Industrial Control
Series Editors
Professor Michael J. Grimble, Professor ofIndustrial Systems and Director
Dr. Michael A. Johnson, Reader in Control Systems and Deputy Director
Industrial Control Centre
Department of Electronic and Electrical Engineering
University of Strathclyde
Graham Hills Building
50 George Street
GlasgowGllQE
United Kingdom
Series Advisory Board
Professor Dr-Ing J. Ackermann
DLR Institut fUr Robotik und Systemdynamik
Postfach 1116
D82230 WeBling
Germany
Professor I.D. Landau
Laboratoire d'Automatique de Grenoble
ENSIEG, BP 46
38402 Saint Martin d'Heres
France
Dr D.C. McFarlane
Department of Engineering
University of Cambridge
Cambridge CB2 lQJ
United Kingdom
Professor B. Wittenmark
Department of Automatic Control
Lund Institute of Technology
PO Box 118
S-221 00 Lund
Sweden
Professor D.W. Clarke
Department of Engineering Science
University of Oxford
Parks Road
Oxford OXI 3PJ
United Kingdom
Professor Dr -Ing M. Thoma
Westermannweg 7
D-30419 Hannover
Germany
Professor H. Kimura
Department of Mathematical Engineering and Information Physics
Faculty of Engineering
The University of Tokyo
7-3-1 Hongo
BunkyoKu
Tokyo 113
Japan
Professor A.J. Laub
College of Engineering -Dean's Office
University of California
One Shields Avenue
Davis
California 95616-5294
United States of America
Professor J.B. Moore
Department of Systems Engineering
The Australian National University
Research School of Physical Sciences
GPO Box 4
Canberra
ACT 2601
Australia
Dr M.K. Masten
Texas Instruments
2309 Northcrest
Plano
TX 75075
United States of America
Professor Ton Backx
AspenTech Europe B.V.
DeWaal32
NL-5684 PH Best
The Netherlands
To Catherine, my wife
Nicola, Nadia and Annick,
our children
SERIES EDITORS' FOREWORD
The series Advances in Industrial Control aims to report and encourage technology
transfer in control engineering. The rapid development of control technology
impacts all areas of the control discipline. New theory, new controllers, actuators,
sensors, new industrial processes, computing methods, new applications, new
philosophies ... , new challenges. Much of the development work resides in
industrial reports, feasibility study papers and the reports of advanced collaborative
projects. The series offers an opportunity for researchers to present an extended
exposition of such new work in all aspects of industrial control for wider and rapid
dissemination.
The potentially devastating effect of an operator making the wrong decision in
the control of a highly automated system or process is well known. However as even
more large-scale automated systems become likely, for example automated
highways for cars, it is increasingly important to be able to assess the safety of these
mixed or joint systems. Carlo Cacciabue's monograph on the modelling and
simulation of these mixed processes of technological systems and human operators
is extremely timely. The monograph provides an up-to-date and systematic
presentation of the basic concepts and tools needed. This comprehensive coverage
of the subject also includes a review of the last twenty years of research effort in the
field. The monograph culminates in two applications of the joint system methods to
two examples concerned with safety in nuclear power plant
It is pleasing to have this monograph in the Series because it is on a very unusual
but important systems topic in control engineering. We hope that Carlo Cacciabue's
careful exposition of mixed technological systems and human factors will stimulate
new avenues of research and new monograph contributions on the subject
M.J. Grimble and M.A. Johnson
Industrial Control Centre
Glasgow, Scotland, UK
FOREWORD
The Simulation Of Joint Systems
The idea of using simulations both to describe systems and to represent their
dynamic properties in such a way that they can be calculated by a computer is far
from new. The foundations of this field were laid in the early 1960s when Kristen
Nygaard and Ole-Johan Dahl began to develop the SIMULA language at the
Norwegian Computing Centre in Oslo. The beginnings were slow, since in the 1960s
both computers and programming languages were relatively novel entities. But in the
years since then, and in particular since the mid-1980s, the use of simulations has
grown at a phenomenal rate. This is mainly due to the fact that computing power has
become a common commodity that is available to anyone. Calculations and
simulations, such as Computational Fluid Dynamics, that would have required the
power of a mainframe less than a decade ago can today be performed by a standard
Personal Computer at a fraction of the cost -both for the hardware and the softw~e!
The use of simulations has therefore grown both in depth and in breadth. The depth
or degree of realism and complexity of simulations has steadily been improved and
constantly seems to challenge the capacity of even the most powerful computers. But
more importantly, the breadth of simulation has grown even faster. Whereas
simulations initially were used for a small number of technically well-developed
domains, such as weather forecasting and operations analysis, they are now present
in almost every kind of human endeavour and have, in particular, stepped out of the
world of mathematics to the more open territory of visualisation and human-machine
interaction.
The greater availability of the computer as a universal scientific tool has been
matched by the development of powerful software tools that have made it less of a
daunting task to attack a new problem. Indeed, specialised tools now exist within
many scientific fields that enable people with technical rather than computing
expertise to develop their own simulation models. Simulations are usually thought of
as part of the technical or engineering sciences, but they are in fact used commonly
in design, training, advertising, political decision making, company planning, etc.
We come across the use of simulations in the daily and scientific press for subjects
as diverse as global warming, quasars and black holes, car crashes, urban pollution,
xii Foreword
global weather phenomena such as EI Nino, stock market developments, simulated
warfare, synthesis of chemicals and compounds, earthquakes, traffic patterns, etc.
The use of simulation has clearly become a common tool for practically every
problem.
The attraction of simulations is that they are easier and cheaper to use than the
real system, e.g. simulation of car crashes. In many cases a simulation may also be
the only way to investigate something either because the target phenomena may be
out of reach, such as quasars, impossible to control, such as global warming or an
earthquake, or simply because they do not exist yet, such as a new car or a drug. The
common basis for a simulation is a model of a phenomenon in a formal language that
is programmed so that the calculations can be performed automatically. In this way it
is possible to see what the consequences of changes in the model parameters will be -
at least according to the assumptions built into the model.
Simulations have understandably been used mostly for phenomena that could be
modelled in precise, and preferably mathematical, terms such as technological
systems or other phenomena that broadly speaking are subject to the laws of nature,
cf. the examples given above. As the use of simulations have grown in breadth, we
have been faced with the challenge of simulating what one might call interactive
systems, i.e., systems where the overall performance is determined by how the sub
systems interact with each other, specifically on how they respond to the behaviour
of other sub-systems. This is, of course, particularly important when one or more of
the sub-systems are living or biological systems rather than technological systems,
especially if the biological system also is a psychological one - i.e., a human being.
Examples of that could be the behaviour of cars on a highway, the behaviour of
people in a train station, the behaviour of the stock market traders, and the behaviour
of a person charged with controlling a dynamic process or machine. As the
simulations of technological systems (using the term broadly) have become better
and better, the challenge to model mixed technological and psychological systems
has grown. In technical terms such systems are often referred to as joint systems, to
emphasise the importance of considering the characteristics of the system as a whole
rather than attributes of the parts. The simulation of joint systems, in fact, represents
not one but two challenges: the first challenge is to model the interaction between
psychological and technological systems, while the second is to model the
psychological system itself.
The modelling of the interaction is a challenge because the simulation must go
beyond a straightforward calculation of the internal state changes in each system.
The interaction is an active exchange of data or information, and in the case of
psychological systems the description of how input is noted and captured is not a
trivial matter. The simulation of the interaction also raises the spectre of
synchronisation in real time, hence of defining what real time is for each sub-system.
The modelling of the psychological system, commonly referred to as operator
modelling, is an even more daunting task. Despite the multitude of systems that have
been built to simulate human cognition - specifically within the field of artificial
intelligence and cognitive science - the results have been relatively meagre. This is
