Adaptive Inverse Control: A Signal Processing Approach, - download pdf or read online

By Mohamed E. El?Hawary(eds.)

ISBN-10: 0470226099

ISBN-13: 9780470226094

ISBN-10: 0470231610

ISBN-13: 9780470231616

A self-contained creation to adaptive inverse control

Now that includes a revised preface that emphasizes the insurance of either keep an eye on platforms and sign processing, this reissued variation of Adaptive Inverse keep an eye on takes a unique process that isn't to be had in the other book.

Written by way of pioneers within the box, Adaptive Inverse keep watch over provides tools of adaptive sign processing which are borrowed from the sphere of electronic sign processing to resolve difficulties in dynamic platforms keep an eye on. This special approach permits engineers in either fields to percentage instruments and strategies. essentially and intuitively written, Adaptive Inverse keep an eye on illuminates thought with an emphasis on useful purposes and common-sense realizing. It covers: the adaptive inverse regulate suggestion; Weiner filters; adaptive LMS filters; adaptive modeling; inverse plant modeling; adaptive inverse keep an eye on; different configurations for adaptive inverse keep watch over; plant disturbance canceling; process integration; Multiple-Input Multiple-Output (MIMO) adaptive inverse regulate structures; nonlinear adaptive inverse regulate structures; and more.

whole with a thesaurus, an index, and bankruptcy summaries that consolidate the data offered, Adaptive Inverse keep watch over is suitable as a textbook for complicated undergraduate- and graduate-level classes on adaptive keep an eye on and in addition serves as a worthwhile source for practitioners within the fields of regulate structures and sign processing.Content:
Chapter 1 The Adaptive Inverse keep watch over proposal (pages 1–39):
Chapter 2 Wiener Filters (pages 40–58):
Chapter three Adaptive LMS Filters (pages 59–87):
Chapter four Adaptive Modeling (pages 88–110):
Chapter five Inverse Plant Modeling (pages 111–137):
Chapter 6 Adaptive Inverse regulate (pages 138–159):
Chapter 7 different Configurations for Adaptive Inverse keep watch over (pages 160–208):
Chapter eight Plant Disturbance Canceling (pages 209–257):
Chapter nine procedure Integration (pages 258–269):
Chapter 10 Multiple?Input Multiple?Output (MIMO) Adaptive Inverse regulate structures (pages 270–302):
Chapter eleven Nonlinear Adaptive Inverse keep an eye on (pages 303–329):
Chapter 12 friendly Surprises (pages 330–338):

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Extra info for Adaptive Inverse Control: A Signal Processing Approach, Reissue Edition

Example text

The purpose is to achieve orthogonalization, which the DFT‘ does well, except for the phenomenon of leakage. Once the tap signals are orthogonalized (approximately) and power normalized, they are weighted and summed to produce the filter output. The weights are adapted by conventional LMS. Although adaptation is done simply with steepest descent, the behavior is similar to adaptation with Newton’s method. Finally, the DCTLMS algorithm is discussed. Structurally, this algorithm is very similar to DFT‘LMS, with the DCT (digital cosine transform) substituted for the DFT’.

Represents expectation. We multiply the time sequence f k by itself, lagged by m time delays, then we average. 3). and the corresponding expansion for G(z) have a common region of absolute convergence in the z-domain. Sec. 1 Digital Filters, Correlation Functions, z-Transforms 43 Input. fk ... 2 A noncausal two-sided digital filter. 11) by making N large, but finite. Given a digital filter like that of Fig. 1 or Fig. 2, the crosscorrelation function between the input f k and the output gk is defined as A @ f g ( m )= E[fk ' gk+ml = @gf(-m).

0 INTRODUCTION Wiener filters are best linear least squares filters which are used for prediction, estimation, interpolation, signal and noise filtering, and so forth. To design them, prior knowledge of the appropriate statistical properties of the input signal(s) is required. The problem is that this prior knowledge is often not available. Adaptive filters are used instead, making use of input data to learn the required statistics. Wiener filter theory is important to us however, because the adaptive filters used here converge asymptotically (in the mean) on Wiener solutions.

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