Methods and Applications of Intelligent Control

This book is concerned with Intelligent Control methods and applications. The field of intelligent control has been expanded very much during the recent years and a solid body of theoretical and practical results are now available. These results have been obtained through the synergetic fusion of concepts and techniques from a variety of fields such as automatic control, systems science, computer science, neurophysiology and operational research. Intelligent control systems have to perform anthropomorphic tasks fully autonomously or interactively with the human under known or unknown and uncertain environmental conditions. Therefore the basic components of any intelligent control system include cognition, perception, learning, sensing, planning, numeric and symbolic processing, fault detection/repair, reaction, and control action. These components must be linked in a systematic, synergetic and efficient way. Predecessors of intelligent control are adaptive control, self-organizing control, and learning control which are well documented in the literature. Typical application examples of intelligent controls are intelligent robotic systems, intelligent manufacturing systems, intelligent medical systems, and intelligent space teleoperators. Intelligent controllers must employ both quantitative and qualitative information and must be able to cope with severe temporal and spatial variations, in addition to the fundamental task of achieving the desired transient and steady-state performance. Of course the level of intelligence required in each particular application is a matter of discussion between the designers and users. The current literature on intelligent control is increasing, but the information is still available in a sparse and disorganized way.

Inhalt

Preface. Part I: Intelligent Control Surveys. 1. Introduction: Overview of Intelligent Controls; S.G. Tzafestas. 2. Fuzzy and Neural Intelligent Control: Basic Principles and Architectures; S.G. Tzafestas, C.S. Tzafestas. 3. Intelligent Control Using Artificial Neural Networks and Fuzzy Logic - Recent Trends and Industrial Applications; A. Zilouchian, et al. 4. Control of Robotic Manipulators Using Neural Networks - A Survey; P. Gupta, N.K. Sinha. Part 2: Intelligent Control Methods. 5. Intelligent Process Control with Supervisory Knowledge-Based Systems; S.J. Kendra, et al. 6. Fuzzy Model Based Predictive Controller; C. Batur, et al. 7. Fuzzy Adaptive Control Versus Model-Reference Adaptive Control of Mutable Processes; I. Skrjanc, D. Matko. 8. Intelligent Control and Supervision Based on Fuzzy Petri Nets; G.K.H. Pang, et al. 9. Off-Line Verification of an Intelligent Control; V.S. Alagar, K. Periyasamy. 10. Intelligent Neurofuzzy Estimators and Multisensor Data Fusion; C.J. Harris, et al. 11. Linguistic Communication Channels; W. Pedrycz. Part 3: Intelligent Control Applications. 12. Intelligent Human-Machine Systems; G. Johannsen, et al. 13. Intelligent Control for a Robotic Acrobat; S.C. Brown, K.M. Passino. 14. Adaptive and Learning Control of Robotic Manipulators; Z. Ahmad, A. Guez. 15. Fuzzy Evolutionary Algorithms and Automatic Robot Trajectory Generation; B.H. Xu, et al. 16. Application of Intelligent Control Techniques inAdvanced AGC; K. Liu, et al. 17. Manufacturing Controller Design and Deadlock Avoidance Using a Matrix Model for Discrete Event Systems; F.L. Lewis, et al. 18. Adaptive, Model-Based Predictive, and Neural Control of the Welding Process; S.G. Tzafestas, et al. Biographies of the Contributors. Index.