Adaptive Control, Filtering, and Signal Processing / Edition 1

by K.J. Aström, G.C. Goodwin, P.R. Kumar

Overview

The area of adaptive systems, which encompasses recursive identification, adaptive control, filtering, and signal processing, has been one of the most active areas of the past decade. Since adaptive controllers are fundamentally nonlinear controllers which are applied to nominally linear, possibly shastic and time-varying systems, their theoretical analysis is usually very difficult. Nevertheless, over the past decade much fundamental progress has been made on some key questions concerning their stability, convergence, performance, and robustness. Moreover, adaptive controllers have been successfully employed in numerous practical applications, and have even entered the marketplace.

Product Details

ISBN-13:	9780387979885
Publisher:	Springer New York
Publication date:	04/27/1995
Series:	The IMA Volumes in Mathematics and its Applications , #74
Edition description:	1995
Pages:	396
Product dimensions:	6.10(w) x 9.25(h) x 0.04(d)

Table of Contents

Oscillations in systems with relay feedback.- Compatibility of shastic and worst case system identification: Least squares, maximum likelihood and general cases.- Some results for the adaptive boundary control of shastic linear distributed parameter systems.- LMS is H? optimal.- Adaptive control of nonlinear systems: A tutorial.- Design guidelines for adaptive control with application to systems with structural flexibility.- Estimation-based schemes for adaptive nonlinear state-feedback control.- An adaptive controller inspired by recent results on learning from experts.- Shastic approximation with averaging and feedback: faster convergence.- Building models from frequency domain data.- Supervisory control.- Potential self-tuning analysis of shastic adaptive control.- Shastic adaptive control.- Optimality of the adaptive controllers.- Uncertain real parameters with bounded rate of variation.- Averaging methods for the analysis of adaptive algorithms.- A multilinear parametrization approach for identification of partially known systems.- Adaptive filtering with averaging.