Robotics Automation

Adaptive Inverse Control: A Signal Processing Approach, by Mohamed E. El?Hawary(eds.)

By Mohamed E. El?Hawary(eds.)

A self-contained advent to adaptive inverse control

Now that includes a revised preface that emphasizes the assurance of either keep watch over platforms and sign processing, this reissued variation of Adaptive Inverse keep an eye on takes a singular method that's not to be had in the other book.

Written via pioneers within the box, Adaptive Inverse keep watch over provides equipment of adaptive sign processing which are borrowed from the sphere of electronic sign processing to unravel difficulties in dynamic platforms keep watch over. This special approach permits engineers in either fields to percentage instruments and methods. truly and intuitively written, Adaptive Inverse regulate illuminates thought with an emphasis on useful functions and common-sense figuring out. It covers: the adaptive inverse keep watch over inspiration; Weiner filters; adaptive LMS filters; adaptive modeling; inverse plant modeling; adaptive inverse keep an eye on; different configurations for adaptive inverse regulate; plant disturbance canceling; method integration; Multiple-Input Multiple-Output (MIMO) adaptive inverse keep an eye on structures; nonlinear adaptive inverse keep an eye on structures; and more.

entire with a thesaurus, an index, and bankruptcy summaries that consolidate the data awarded, Adaptive Inverse keep watch over is suitable as a textbook for complex undergraduate- and graduate-level classes on adaptive keep an eye on and in addition serves as a invaluable source for practitioners within the fields of keep an eye on structures and sign processing.Content:
Chapter 1 The Adaptive Inverse regulate idea (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 process Integration (pages 258–269):
Chapter 10 Multiple?Input Multiple?Output (MIMO) Adaptive Inverse keep watch over platforms (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 resources for Adaptive Inverse Control: A Signal Processing Approach, Reissue Edition

Example text

The plant output disturbance (which is computed as the difference between the plant output and what the plant output would be if there were no disturbance) is squared at each sample time and plotted in Fig. 28. There is no averaging in this plot, as the squared values are plotted over time. The disturbance canceling feedback loop was open until the five-thousandth sample in the time sequence. During this epoch, there was more than enough time for adaptive modeling of the equivalent plant and adaptive inverse modeling of this equivalent plant.

Vol. 3, Paper 25C- 1, 1971, p. 261. [61] B. WIDROW,“Adaptive model control applied to real-time blood-pressure regulation,” in Pattern Recognition and Machine Learning; proceedings, ed. FU(New York: Plenum Press, 1971). pp. 310-324. [62] B. WIDROW, J. MCCOOL,and B. MEDOFF,“Adaptivecontrol by inverse modeling,” in Con$ Rec. of I2thAsilomr Conference on Circuits, Systems and Computers, Santa Clara, CA, November 1978, pp. 90-94. [63] B. WIDROW,D. SHUR, and S. SHAFFER,“On adaptive inverse control,” in Con$ Rec.

A Volterra filter may be adapted by the LMS algorithm. A neural network filter would generally be adapted by the backpropagation algorithm of Werbos [78], and Rumelhart and colleagues [79], [80]. Backpropagation is the most widely used training algorithm for neural networks worldwide. It is an outgrowth of and a substantial generalization of the LMS algorithm. How to do nonlinear plant modeling, with and without dither, is described in this chapter. When using dither, the superposed natural plant command signals, which could be nonstationary and which sometimes could be larger or sometimes smaller than the dither, mix nonlinearly in the plant.

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