By Mohamed E. El?Hawary(eds.)
A self-contained advent to adaptive inverse control
Now that includes a revised preface that emphasizes the insurance of either keep an eye on structures and sign processing, this reissued version of Adaptive Inverse keep an eye on takes a singular process that's not on hand in the other book.
Written via pioneers within the box, Adaptive Inverse keep watch over offers tools of adaptive sign processing which are borrowed from the sphere of electronic sign processing to resolve difficulties in dynamic platforms keep watch over. This special approach permits engineers in either fields to percentage instruments and strategies. truly and intuitively written, Adaptive Inverse keep an eye on illuminates thought with an emphasis on functional purposes and common-sense knowing. It covers: the adaptive inverse keep an eye on suggestion; Weiner filters; adaptive LMS filters; adaptive modeling; inverse plant modeling; adaptive inverse keep watch over; different configurations for adaptive inverse keep watch over; plant disturbance canceling; procedure integration; Multiple-Input Multiple-Output (MIMO) adaptive inverse keep watch over structures; nonlinear adaptive inverse keep an eye on platforms; and more.
whole with a thesaurus, an index, and bankruptcy summaries that consolidate the data offered, Adaptive Inverse keep an eye on is suitable as a textbook for complex undergraduate- and graduate-level classes on adaptive keep watch over and likewise serves as a helpful source for practitioners within the fields of keep watch over platforms and sign processing.Content:
Chapter 1 The Adaptive Inverse regulate thought (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 platforms (pages 270–302):
Chapter eleven Nonlinear Adaptive Inverse regulate (pages 303–329):
Chapter 12 friendly Surprises (pages 330–338):
Read or Download Adaptive Inverse Control: A Signal Processing Approach, Reissue Edition PDF
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Additional resources for Adaptive Inverse Control: A Signal Processing Approach, Reissue Edition
In discrete form, Wiener theory is appropriate for analyzing digital adaptive filters. Causal and noncausal Wiener filters are studied. Both forms are used to analyze the behavior of adaptive filters as inverse controllers and plant disturbance cancelers. A very simple approach to causal Wiener filtering was devised by Bode and Shannon . Shannon-Bode theory is explained in detail This theory is extremely useful in the development of adaptive inverse control. Chapter 3: Adaptive L M S Filters This chapter reviews the theory of adaptive digital filtering, which is essential to the development of adaptive inverse control.
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). 14) =hm *@ff(m). Therefore, the crosscorrelation between input and output of a linear digital filter is the convolution of the input autocomelation function with the impulse response. The same result is obtained for causal or noncausal filters. 44 Wiener Filters Chap. 14), we have @f&) = H ( z ) . @ff (z). 17) is similar to a convolution. ie a time-reversed impulse response as - - A A hl = h-1, or h-1 = hr.
30 Entire learning sequence of 10,000 samples. Plot of error, equal to difference between reference model output and plant output. Disturbance canceler turned on at 5,000 samples. 31 Square of overall system error plotted without averaging, over entire 10,000-sample learning sequence. Disturbance canceler turned on at 5,000 samples. The Adaptive Inverse Control Concept 24 Chap. ) Figure 132 Comparison of reference model output with plant output over last 200 samples of training sequence. Controller has long ago converged.
Adaptive Inverse Control: A Signal Processing Approach, Reissue Edition by Mohamed E. El?Hawary(eds.)