An Introduction to intelligent and autonomous control
著者
書誌事項
An Introduction to intelligent and autonomous control
Kluwer Academic, c1993
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
The area of intelligent control is a fusion of a number of research areas in engineering computer science and mathematics, which has evolved from conventional control to enhance the existing nonlinear, optimal, adaptive and stochastic control methods. Intelligent control techniques are currently being utilized for closed-loop feedback control in space-based applications, manufacturing systems, robotic systems, avionic systems, among others, to improve system performance, reliability and efficiency. Overall, the primary objective of intelligent control is to enhance the performance of the system to the extent that it achieves some level of autonomous control. This work provides an introduction to, and survey of, the vital and emerging area of intelligent control by leading researchers in the area. Contributors to "An Introduction to Intelligent and Autonomous Control" are world-wide experts who have been invited on the strength of their research. The fundamental theory, archictectures and perspectives on intelligent control are presented.
Approaches to intelligent control, including expert control, planning systems, fuzzy control, neural control and learning control are studied in detail. Applications are introduced via robotic systems, avionic systems and failure diagnosis for process operations. "An Introduction to Intelligent and Autonomous Control" is designed as a reference for professionals and academic researchers and may also be used as the foundation for graduate level courses on intelligent and autonomous control.
目次
- Part 1 Theory and architectures: introduction to intelligent control systems with high degrees of autonomy, P.A. Antsaklis and K.M. Passino
- a reference model architecture for intelligent systems design, J.S. Albus
- model-based architecture concepts for autonomous systems design and simulation, B.P. Zeigler and Sungdo Chi
- design of structure-based hierarchies for distributed intelligent control, L. Acar and U. Ozguner
- modelling and design of distributed intelligence systems, A.H. Levis
- nested hierarchical control, A. Meystel. Part 2 Design approaches and techniques: expert control, K.J. Astrom and K.-E. Arzen
- modelling and analysis of artificially intelligent planning systems, K.M. Passino and P.J. Antsaklis
- fuzzy and neural control, H.R. Berenji
- learning control systems, J.A. Farrell and W. Baker
- learning control methods, needs and architectures, M.M. Kokar
- learning in control, E. Grant. Part 3 Applications: intellingent robot prehension, Thang N. Nguyen and H. Stephanou
- modelling of multi-sensory robotic systems with failure diagnostic capabilities, K.P. Valavanis and G. Seetharaman
- AUTOCREW - a paradigm for intelligent flight control, B.L. Belkin and R.F. Stengel
- a framework for knowledge-based diagnosis in process operations, P.R. Prased and J.F. Davis.
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