Modelling and Simulation of Human Behaviour in System Control PDF
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Advances in Industrial Control Springer London Berlin Heidelberg New¥ork Barcelona Budapest Hong Kong Milan Paris Santa Clara Singapore Tokyo 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
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