Adaptive Control, Filtering, and Signal Processing
Published on 22. November 2012
Book
Paperback/Softback
XVIII, 396 pages
978-1-4612-6439-2 (ISBN)
Description
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 stochastic 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.
More details
Series
Edition
Softcover reprint of the original 1st ed. 1995
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Illustrations
XVIII, 396 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 23 mm
Weight
628 gr
ISBN-13
978-1-4612-6439-2 (9781461264392)
DOI
10.1007/978-1-4419-8568-2
Schweitzer Classification
Other editions
Additional editions
K.J. Aström | G.C. Goodwin | P.R. Kumar
Adaptive Control, Filtering, and Signal Processing
Book
04/1995
Springer
€160.49
Shipment within 5-7 days
Content
Oscillations in systems with relay feedback.- Compatibility of stochastic and worst case system identification: Least squares, maximum likelihood and general cases.- Some results for the adaptive boundary control of stochastic 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.- Stochastic approximation with averaging and feedback: faster convergence.- Building models from frequency domain data.- Supervisory control.- Potential self-tuning analysis of stochastic adaptive control.- Stochastic 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.