Performance of Nonlinear Approximate Adaptive ControllersIn 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. |
Contents
39 | 13 |
Uncertainty Modelling Control Design and System Performance | 91 |
1 | 121 |
Copyright | |
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Performance of Nonlinear Approximate Adaptive Controllers Mark French,Csaba Szepesvári,Eric Rogers Limited preview - 2003 |
Common terms and phrases
adaption gain adaptive control adaptive damping adaptive design approximation error assume asymptotic tracking B-splines backstepping basis functions Besov space boundedness Chapter closed loop construction continuous function control design control effort convergence dead-zone denote differential equation dimension function dmax domain estimate example exists follows function approximation model function approximator function f ƒº given global Gram matrix hence initial condition integrator chain Jackson inequality Lemma lim sup linear Lyapunov equation Lyapunov function nonlinear norm normal form out-perform output feedback P(Exo parameter polynomial positive definite problem proof properties Proposition resolution divergent resolution scaleable result satisfies sequence smoothness space solution stabilisation stability strict feedback sup sup sup Suppose T₁ Theorem trajectory transient performance uncertainty level uncertainty model uncertainty set uniformly bounded vector Vref whilst worst-case Yref მყ