Performance of Nonlinear Approximate Adaptive Controllers

Product Description In recent years, there has been a wide interest in non-linear adaptive control using approximate models, either for tracking or regulation, and usually under the banner of neural network based control. The authors present a unique critical evaluation of the approximate model philosophy and its setting, rigorously comparing the performance of such controls against competing designs. Analysing a very topical aspect of contemporary research and control practice, this book highlights the situations in which approximate model based designs are most appropriate and indicates scenarios in which other designs could be used more productively. Throughout the text concepts are illustrated using a variety of examples, both academic problems and those based on physical examples. The work is designed to open the door to realistic applications. It provides a unified coverage of the theory and application of a wide range of control systems areas including neural network based control and control using the approximate model. It presents a mathematically well founded introduction to the area of intelligent control. It provides a varied selection of practical examples drawn from a variety of fields, including robotics and aerospace, illustrate theoretical principles. It gives clear comparisons of a variety of control designs and offers cross disciplinary approach to this leading edge topic. It is a valuable reference for control practitioners and theorists, artificial intelligence researchers and applied mathematicians, as well as graduate students and researchers with an interest in adaptive control and stability. Review "I was attracted by this proposal. It is a relatively new area, the authors are well-respected, the book should make a useful contribution to the literature, and will have a reasonable 'shelf life'....There is no book at present taking their approach.," -- David Clarke, Professor of, University of Oxford, UK "It would be a useful contribution to the literature.....I can see it as a useful reference book for graduate students and researchers working on related areas.,"-- Jing Sun, Ford "This is a difficult but important area of research. The book might be of interest to both non-linear and adaptive control theorists but also to people who work on control via neural networks....the fact that it builds upon the control designs in our book, which has sold well, might attract a part of its readership."-- Professor Miroslav Krstic, Department of Mechanical & Aerospace Engineering, University of California, San Diego, USA From the Back Cover In recent years there has been wide interest in nonlinear adaptive control using function approximator models, either for tracking or regulation. Such techniques are often described as 'neural network based control'. In this book, the authors present a critical evaluation of the approximate model philosophy and its setting, showing situations in which its designs can be expected to surpass rival algorithms.Describing complex concepts with a variety of theoretical and practical illustrations, this book also:Develops a comprehensive set of performance based adaptive control resultsProvides a constructive framework for estimating both upper and lower bounds of performance cost functionalsPresents theoretical principles and a mathematically well-founded introduction to the area of 'intelligent control'Featuring a &;toolkit&; that allows quantitative comparisons between competing control designs, Performance of Nonlinear Approximate Adaptive Controllers will prove an invaluable reference for control practitioners and theorists, AI researchers, applied mathematicians and graduate students in adaptive control and stability. About the Author Mark French is the author of Performance of Nonlinear Approximate Adaptive Controllers, published by Wiley. Csaba Szepesvári is the author of Performance of Nonlinear Approximate Adaptive Controlle