Performance of Nonlinear Approximate Adaptive Controllers

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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 results Provides a constructive framework for estimating both upper and lower bounds of performance cost functionals Presents 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.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. * 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 * Presents a mathematically well founded introduction to the area of intelligent control * A varied selecion of practical examples drawn from a variety of fields, including robotics and aerospace, illustrate theoretical principles * Clear compaisons of a variety of control designs * Cross disciplinary approach to this leading edge topic 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.