an introduction to intelligent and autonomous control

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Table of Contents

Preface: George N. Saridis.....ix

Overview: Panos J. Antsaklis and Kevin M. Passino.....xi

Part I: Theory and ArchitecturesChapter1:Introduction to Intelligent Control Systems with High Degrees of Autonomy.....1

Panos J. Antsaklis and Kevin M. Passino

Chapter 2: A Reference Model Architecture for Intelligent Systems Design.....27James S. Albus

Chapter 3:Model-Based Architecture Concepts for Autonomous Systems Design andSimulation.....57Bernard P. Zeigler and Sungdo Chi

Chapter 4:Design of Structure-Based Hierarchies for Distributed Intelligent Control.....79Levent Acar and Umit Ozguner

Chapter 5: Modeling and Design of Distributed Intelligence Systems.....109Alexander H. Levis

Chapter 6: Nested Hierarchical Control.....129Alex Meystel

Part II: Design Approaches and TechniquesChapter 7: Expert Control.....163

Karl J. Astrom and Karl-Erik Arzen

Chapter 8: Modeling and Analysis of Artificially Intelligent Planning Systems.....191

Kevin M. Passino and Panos J. Antsaklis

Chapter 9: Fuzzy and Neural Control.....215

Hamid R. BerenjiChapter 10: Learning Control Systems.....237

Jay Farrell and Walter Baker

Chapter 11: Learning Control: Methods, Needs and Architectures.....263

Mieczyslaw M. Kokar

Chapter 12: Learning in Control.....283

Edward Grant

Part III: ApplicationsChapter 13: Intelligent Robot Prehension.....319

Thang N. Nguyen and Harry Stpehanou

Chapter 14: Modeling of MultiSensory Robotic Systems with Failure DiagnosticCapabilities.....349Guna Seetharaman and Kimon P. Valavanis

Chapter 15: AUTOCREW: A Paradigm for Intelligent Flight Control.....371Brenda L. Belkin and Robert F. Stengel

Chapter 16: A Framework for Knowledge-Based Diagnosis in Process Operations.....401P.R. Prasad and James F. Davis

Index .....423

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Overview

The ever increasing technological demands of today call for very complex systems, whichin turn require highly sophisticated controllers to ensure that high performance can be achievedand maintained under adverse conditions. There are needs in the control of these complexsystems which cannot be met by conventional controllers and this is primarily due to the lack of

precise knowledge about the process to be controlled. Acquisition of adequate system knowledgeis often problematic or impractical due to system complexity and the fact that the structure andparameters in many systems change in significant and unpredictable ways over time. Moreover,to reduce design complexity control designers often utilize less detailed models for control thanwhat they have access to. To address the control demands of such highly complex and uncertainsystems one can enhance today's control methods using intelligent control systems andtechniques.

The area of Intelligent Control is a fusion of a number of research areas in Systems andControl, Computer Science and Operations Research among others, coming together, merging

and expanding in new directions and opening new horizons to address the new problems of thischallenging and promising area. Perhaps in the not so distant future we will be able to witnesscontrol systems which perform many intelligent functions so that they exhibit high degrees ofautonomy and hence release us from dangerous or mundane tasks. Intelligent control systems aretypically able to perform one or more of the following functions: planning actions at differentlevels of detail, learning from past experience, identifying changes that threaten the systembehavior, such as failures, and reacting appropriately. This identifies the areas of Planning andExpert Systems, Fuzzy Systems, Neural Networks, Machine Learning, and Failure Diagnosis, tomention but a few, as existing research areas that are related and important to Intelligent Control.In addition, there are novel problems raised in the context of machine intelligence and intelligent

control, the solution of which requires the development of completely new approaches andmethods- such is the case for example in the study of symbolic-numeric / discrete andcontinuous state systems called "Hybrid Systems."

The Chapters in this book are written by the specialists in the area. They discuss a varietyof approaches, theories, solved and unsolved problems, fundamental and advanced methods, anda number of applications. The contributions are grouped into three parts: Part I deals with thetheories, fundamentals, architectures and the different ways of looking at Intelligent Control.Part 11 addresses specific classes of problems and suggests particular methods for IntelligentControl such as expert, planning, fuzzy, neural, and learning systems. Part 111 explicitly dealswith applications. Applications are in fact diffused throughout the chapters of the book in theform of examples for proof of concept or for illustration of ideas.

Overall, this book provides a solid introduction to the area. Studying the material one iscertainly impressed by the gains so far, by the amount of pioneering work which has been, andcurrently is being performed but also by the enormity of the undertaking. It is quite clear that weare at the very early stages of development in this important and extremely promising field. Thedifficulties sometimes appear unsurpassed but the rewards are also without precedent, trulyfantastic.

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It is essential to have a view of the whole area, of the goals, theories, approaches, differentviews, and architectures. This is presented in the six chapters of Part I. Such an overview of thearea, which is in fact a Systems' approach, is very helpful indeed as it provides the framework tobuild upon. This framework has to be flexible to accommodate new ideas but it must also befirm enough to provide good foundations. The theories presented in Part I are different enough,but also similar enough for one to be able to identify the main concepts and ideas of intelligent

control that are common to all.

The first paper in Part I provides an introduction to the field and discusses the mainconcepts and ideas. A general approach to design and control of intelligent systems is given inchapters two and three. Distributed intelligent control is discussed next while distributedintelligence systems, such as human organizations, are studied in chapter five. Chapter sixdescribes a rather distinct approach to design and control of intelligent systems. We provide abrief overview of each of the chapters below:

Antsaklis and Passino provide an introduction to control systems that incorporate intelligentmethods to attain higher degrees of autonomy. An overview of the field of intelligent control isprovided, a hierarchical functional architecture is discussed, and the characteristics of suchsystems are explained. It is emphasized that: (i) the area of intelligent control is interdisciplinary,requiring research in and coordination among many research disciplines, and (ii) there is needfor analytical models and methods to study intelligent control systems and recent advances in thearea of discrete event and hybrid dynarnical systems do offer significant promise.

Albus describes an architecture for intelligent system design based on his RCSReal time ControlSystem-reference model architecture; in RCS the types of functions that are required in a real

time intelligent control system are defined together with how these functions relate to each other,thus providing a structural approach to design. This systematic approach has been used in avariety of applications such as in controlling machining workstations, space station telerobots,autonomous underwater vehicles, and in postal service mail handling.

Zeigler and Chi present a model-based architecture for the design and simulation of highautonomy systems. A model base is at the center of its planning, operation, diagnosis and faultrecovery strategies; timing effects are also being considered as they are so important for thecorrect operation of the system. The general approach is characterized via a "hierarchicalencapsulation and abstraction principle". As a proof of concept, this approach is implemented to

automate a space-borne laboratory.

Acar and Ozguner, based on distributed control concepts, describe a structure-based hierarchyfor the intelligent control of systems; the mathematical foundations are stressed and analyticalresults are presented. This approach is different in that its organization is based on the physicalstructure of a system rather than on its functionality. The approach is illustrated on roboticsapplications.

Levis discusses distributed intelligence systems such as human organizations. He describes an

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emerging mathematical theory for the analysis and design of such systems; an informationprocessing and decision making system approach is taken where both human and machineintelligence coexist and interact. The theory is showing that such systems can exhibit a widevariety of not well understood behavior.

Finally nested hierarchical control its foundations and history is presented by Meystel. It isdescribed as a development of theories of multiresolutional image and signal representation into

the domain of control theory that enables efficient practice of design and control using nestedsearch in state space.

Part II addresses particular questions and aspects of intelligent control. As it was mentionedearlier one recognizes the research areas of expert and planning systems fuzzy systems neuralnetworks and learning systems as providing several methods for the intelligent control ofcomplex systems. All of these are addressed by the six chapters in Part II. Expert and planningsystems are considered in the first two chapters while fuzzy and neural control is the topic of thenext chapter. The remaining three chapters all discuss aspects of learning in control reflecting thegreat importance of learning methods in intelligent control. In particular we provide a briefoverview of each of the chapters of Part II below:

Astrom and Arzen describe how to use expert systems to implement control functions. Theyexplain how such expert controllers can automate several tasks normally performed by operatorsand process and control engineers by emulating their heuristic decision making activities. Anoverview of the foundations of the area of expert control systems is provided andimplementation issues are discussed. It is explained how the expert control framework can becomposed of many conventional and intelligent control techniques.

Passino and Antsaklis develop a foundation of fundamental control theoretic concepts for themodeling and analysis of AI planning systems. They introduce the ideas of feedforward andfeedback planning systems and highlight the importance of analyzing the dynamical behavior ofAI systems. Finally they show that discrete event system theoretic techniques can be used for themodeling and analysis of simple AI planning problems.

Berenji provides an introduction to fuzzy and neural control. He explains the basic architectureand operation of fuzzy controllers and how they implement interpolative reasoning. Hierarchicalfuzzy control and applications of fuzzy control are discussed. For neural approaches he explainsreinforcement leanzing in control and introduces hybrid fuzzy/neural control via the approximate

reasoningbased intelligent controller. A cart-pole balancing problem is discussed for proof ofconcept.

Farrell and Baker provide an introduction to the area of learning control. They present anextensive overview of the literature and a careful comparison between learning and adaptivesystems. They identify the advantages of augmenting conventional control approaches withlearning capabilities. In addition they illustrate the application of contemporary learning controlapproaches to two examples. Finally the authors provide future research directions.

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Kokar shows how architectures for adaptive control systems have evolved into ones for learningcontrol. The author emphasizes that learning controllers should be able to learn goals models andcontrol laws. In addition he explains the COPER/IC architecture and overviews his contributionswithin this framework. Finally he outlines future needs in the area of learning control.

Grant also describes techniques for learning control. In particular he discusses human controlpassive learning and machine learning. He establishes the heuristics for control of the pole-cart

system and studies the acquisition of knowledge for learning controllers. He provides bothsimulations and results from experiments in learning and neural control.

The emphasis in the last four chapters of the book is on particular applications of intelligentcontrol. It is important to clearly demonstrate that the approaches and ideas which werediscussed can successfully address real needs and this is done in Part III. The first two chaptersof Part III deal with robotic systems while the last two describe particular applications ofintelligent control methods to flight control and fault diagnosis in process control respectively. Inparticular we provide a brief overview of each of the chapters of Part III below:

Nguyen and Stephanou discuss an intelligent integrated symbolic-numeric technique fordexterous manipulation. In particular the authors use topological models and topologicalreasoning for a multifingered robot hand. They show that with this approach both the handposture and functionality can be derived from the high level symbolic requirements andtranslated into low level numeric joint space variables. The application nicely illustrates the useof multilayer hierarchical intelligent control developed from both symbolic and numericmodels/algorithms.

Seetharaman and Valavanis study multi-sensory robotic systems with failure diagnostic

capabilities. In particular the authors investigate the problem of recovery from visual failureswhich occur in multi-sensory robotic systems. In addition they examine problems with sensorfusion in the multi-sensory system. The authors adhere to the classical three level hierarchicalapproach to intelligent control focus on the function and complexity of the vision system andexplain the variety of problems that their approach applies to.

Belkin and Stengel describe an expert system that aids a pilot of an aircraft by automatingdecision making tasks that are normally the responsibility of the crew. In particular the authorsintroduce a cooperative rule-based expert systems approach to intelligent flight control. Theystudy the interactions between the expert systems which automate various functions and study

the performance of the entire AUTOCREW system in terms of its ability to assist the pilot.Extension of the results to other applications is discussed. The approach provides an illustrationof several of the fundamental ideas in intelligent control including the division of responsibilitiesand coordination of intelligent system activities.

Prasad and Davis study how to enhance the capabilities of process control computers toautomate the activity of failure diagnosis. The diagnostic activity is broken down into a set ofinformation-processing tasks and it is explained how such tasks are integrated to form aframework to construct knowledge-based diagnostic systems. Moreover, it is shown how the

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approach facilitates the integration of diverse techniques for automated diagnosis. Overall, thework shows how various intelligent control techniques can be integrated to perform a complextask for a difficult problem facing a wide variety of industries today.

This book represents the collective effort of many researchers in the field of intelligentcontrol who thought that such book would be helpful in introducing the major topics in the areaand providing a description of the state-of-the-art techniques. We would like to thank all the

contributors who dedicated a significant amount of time and effort to make such an enterprisepossible. We sincerely hope that the reader finds the study of this book rewarding and sees it asthe beginning of many exciting and challenging new endeavors.

Panos Antsaklis, Notre Dame, Indiana also London, England and Athens, Greece

Kevin Passino, Columbus, Ohio

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an introduction to intelligent and autonomous control

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