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E-raamat: Adaptive Inverse Control: A Signal Processing Approach

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 11-Dec-2007
  • Kirjastus: Wiley-IEEE Press
  • Keel: eng
  • ISBN-13: 9780470231609
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Adaptive Inverse Control: A Signal Processing ApproachSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 11-Dec-2007
  • Kirjastus: Wiley-IEEE Press
  • Keel: eng
  • ISBN-13: 9780470231609
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Widrow (electrical engineering, Stanford U.) and Walach (director and senior researcher, R&D management, IBM Haifa Research Labs, Israel) borrow methods of adaptive signal processing from the field of digital signal processing and apply them to problems of dynamic systems control, showing how they can be used to control plant dynamics and to minimize the effects of plant disturbance with optimal least squares methods such that dynamic control and disturbance canceling can be optimized without interfering with each other. Chapters cover the adaptive inverse control concept, Wiener filters, adaptive least-mean-square filters, adaptive modeling, inverse plant modeling, adaptive inverse control, other configurations for adaptive inverse control, plant disturbance canceling, system integration, multiple-input multiple-output adaptive inverse control systems, and nonlinear adaptive inverse control. This work first appeared in 1996 as Adaptive Inverse Control, with no subtitle. Annotation ©2008 Book News, Inc., Portland, OR (booknews.com)

A self-contained introduction to adaptive inverse control

Now featuring a revised preface that emphasizes the coverage of both control systems and signal processing, this reissued edition of Adaptive Inverse Control takes a novel approach that is not available in any other book.

Written by two pioneers in the field, Adaptive Inverse Control presents methods of adaptive signal processing that are borrowed from the field of digital signal processing to solve problems in dynamic systems control. This unique approach allows engineers in both fields to share tools and techniques. Clearly and intuitively written, Adaptive Inverse Control illuminates theory with an emphasis on practical applications and commonsense understanding. It covers: the adaptive inverse control concept; Weiner filters; adaptive LMS filters; adaptive modeling; inverse plant modeling; adaptive inverse control; other configurations for adaptive inverse control; plant disturbance canceling; system integration; Multiple-Input Multiple-Output (MIMO) adaptive inverse control systems; nonlinear adaptive inverse control systems; and more.

Complete with a glossary, an index, and chapter summaries that consolidate the information presented, Adaptive Inverse Control is appropriate as a textbook for advanced undergraduate- and graduate-level courses on adaptive control and also serves as a valuable resource for practitioners in the fields of control systems and signal processing.

Arvustused

"Clearly and intuitively written, the book illuminates theory with an emphasis on practical applications and commonsense understanding .A valuable resource." (Bioautomation Journal, October 2008)

Preface xv
The Adaptive Inverse Control Concept
1(39)
Introduction
1(1)
Inverse Control
2(5)
Sample Applications of Adaptive Inverse Control
7(15)
An Outline or Road Map for This Book
22(18)
Bibliography
33(7)
Wiener Filters
40(19)
Introduction
40(1)
Digital Filters, Correlation Functions, z-Transforms
40(5)
Two-Sided (Unconstrained) Wiener Filters
45(6)
Shannon-Bode Realization of Causal Wiener Filters
51(6)
Summary
57(2)
Bibliography
57(2)
Adaptive LMS Filters
59(29)
Introduction
59(1)
An Adaptive Filter
60(1)
The Performance Surface
61(1)
The Gradient and the Wiener Solution
62(2)
The Method of Steepest Descent
64(1)
The LMS Algorithm
65(2)
The Learning Curve and Its Time Constants
67(1)
Gradient and Weight-Vector Noise
67(2)
Misadjustment Due to Gradient Noise
69(2)
A Design Example: Choosing Number of Filter Weights for an Adaptive Predictor
71(3)
The Efficiency of Adaptive Algorithms
74(3)
Adaptive Noise Canceling: A Practical Application for Adaptive Filtering
77(4)
Summary
81(7)
Bibliography
84(4)
Adaptive Modeling
88(23)
Introduction
88(2)
Idealized Modeling Performance
90(1)
Mismatch Due to Use of FIR Models
91(2)
Mismatch Due to Inadequacies in the Input Signal Statistics; Use of Dither Signals
93(4)
Adaptive Modeling Simulations
97(5)
Summary
102(9)
Bibliography
108(3)
Inverse Plant Modeling
111(27)
Introduction
111(1)
Inverses of Minimum-Phase Plants
111(2)
Inverses of Nonminimum-Phase Plants
113(4)
Model-Reference Inverses
117(3)
Inverses of Plants with Disturbances
120(6)
Effects of Modeling Signal Characteristics on the Inverse Solution
126(1)
Inverse Modeling Error
126(2)
Control System Error Due to Inverse Modeling Error
128(2)
A Computer Simulation
130(1)
Examples of Offline Inverse Modeling of Nonminimum-Phase Plants
131(5)
Summary
136(2)
Adaptive Inverse Control
138(22)
Introduction
138(3)
Analysis
141(3)
Computer Simulation of an Adaptive Inverse Control System
144(3)
Simulated Inverse Control Examples
147(7)
Application to Real-Time Blood Pressure Control
154(5)
Summary
159(1)
Bibliography
159(1)
Other Configurations for Adaptive Inverse Control
160(49)
Introduction
160(1)
The Filtered-X LMS Algorithm
160(5)
The Filtered-ε LMS Algorithm
165(5)
Analysis of Stability, Rate of Convergence, and Noise in the Weights for the Filtered-ε LMS Algorithm
170(5)
Simulation of an Adaptive Inverse Control System Based on the Filtered-ε LMS Algorithm
175(5)
Evaluation and Simulation of the Filtered-X LMS Algorithm
180(3)
A Practical Example: Adaptive Inverse Control for Noise-Canceling Earphones
183(3)
An Example of Filtered-X Inverse Control of a Minimum-Phase Plant
186(2)
Some Problems in Doing Inverse Control with the Filtered-X LMS Algorithm
188(6)
Inverse Control with the Filtered-X Algorithm Based on DCT/LMS
194(3)
Inverse Control with the Filtered-ε Algorithm Based on DCT/LMS
197(4)
Summary
201(8)
Bibliography
208(1)
Plant Disturbance Canceling
209(49)
Introduction
209(2)
The Functioning of the Adaptive Plant Disturbance Canceler
211(1)
Proof of Optimality for the Adaptive Plant Disturbance Canceler
212(3)
Power of Uncanceled Plant Disturbance
215(1)
Offline Computation of Qk(z)
215(1)
Simultaneous Plant Modeling and Plant Disturbance Canceling
216(7)
Heuristic Analysis of Stability of a Plant Modeling and Disturbance Canceling System
223(3)
Analysis of Plant Modeling and Disturbance Canceling System Performance
226(3)
Computer Simulation of Plant Modeling and Disturbance Canceling System
229(5)
Application to Aircraft Vibrational Control
234(2)
Application to Earphone Noise Suppression
236(1)
Canceling Plant Disturbance for a Stabilized Minimum-Phase Plant
237(11)
Comments Regarding the Offline Process for Finding Q(z)
248(1)
Canceling Plant Disturbance for a Stabilized Nonminimum-Phase Plant
249(5)
Insensitivity of Performance of Adaptive Disturbance Canceler to Design of Feedback Stabilization
254(1)
Summary
255(3)
System Integration
258(12)
Introduction
258(1)
Output Error and Speed of Convergence
258(3)
Simulation of an Adaptive Inverse Control System
261(5)
Simulation of Adaptive Inverse Control Systems for Minimum-Phase and Nonminimum-Phase Plants
266(2)
Summary
268(2)
Multiple-Input Multiple-Output (MIMO) Adaptive Inverse Control Systems
270(33)
Introduction
270(1)
Representation and Analysis of MIMO Systems
270(4)
Adaptive Modeling of MIMO Systems
274(11)
Adaptive Inverse Control for MIMO Systems
285(5)
Plant Disturbance Canceling in MIMO Systems
290(2)
System Integration for Control of the MIMO Plant
292(4)
A MIMO Control and Signal Processing Example
296(5)
Summary
301(2)
Nonlinear Adaptive Inverse Control
303(27)
Introduction
303(1)
Nonlinear Adaptive Filters
303(4)
Modeling a Nonlinear Plant
307(4)
Nonlinear Adaptive Inverse Control
311(8)
Nonlinear Plant Disturbance Canceling
319(2)
An Integrated Nonlinear MIMO Inverse Control System Incorporating Plant Disturbance Canceling
321(2)
Experiments with Adaptive Nonlinear Plant Modeling
323(3)
Summary
326(4)
Bibliography
329(1)
Pleasant Surprises
330(165)
Stability and Misadjustment of the LMS Adaptive Filter
339(10)
Time Constants and Stability of the Mean of the Weight Vector
339(3)
Convergence of the Variance of the Weight Vector and Analysis of Misadjustment
342(4)
A Simplified Heuristic Derivation of Misadjustment and Stability Conditions
346(1)
Bibliography
347(2)
Comparative Analyses of Dither Modeling Schemes A, B, and C
349(14)
Analysis of Scheme A
350(1)
Analysis of Scheme B
351(1)
Analysis of Scheme C
352(4)
A Simplified Heuristic Derivation of Misadjustment and Stability Conditions for Scheme C
356(2)
A Simulation of a Plant Modeling Process Based on Scheme C
358(1)
Summary
359(3)
Bibliography
362(1)
A Comparison of the Self-Tuning Regulator of Astrom and Wittenmark with the Techniques of Adaptive Inverse Control
363(6)
Designing a Self-Tuning Regulator to Behave like an Adaptive Inverse Control System
364(2)
Some Examples
366(1)
Summary
367(1)
Bibliography
368(1)
Adaptive Inverse Control for Unstable Linear SISO Plants
369(14)
Dynamic Control of Stabilized Plant
370(2)
Adaptive Disturbance Canceling for the Stabilized Plant
372(6)
A Simulation Study of Plant Disturbance Canceling: An Unstable Plant with Stabilization Feedback
378(4)
Stabilization in Systems Having Both Discrete and Continuous Parts
382(1)
Summary
382(1)
Orthogonalizing Adaptive Algorithms: RLS, DFT/LMS, and DCT/LMS
383(13)
The Recursive Least Squares Algorithm (RLS)
384(2)
The DFT/LMS and DCT/LMS Algorithms
386(8)
Bibliography
394(2)
A MIMO Application: An Adaptive Noise-Canceling System Used for Beam Control at the Stanford Linear Accelerator Center
396(13)
Introduction
396(1)
A General Description of the Accelerator
396(3)
Trajectory Control
399(1)
Steering Feedback
400(2)
Addition of a MIMO Adaptive Noise Canceler to Fast Feedback
402(2)
Adaptive Calculation
404(2)
Experience on the Real Accelerator
406(1)
Acknowledgements
407(1)
Bibliography
407(2)
Thirty Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation
409(66)
Introduction
409(3)
Fundamental Concepts
412(16)
Adaptation --- The Minimal Disturbance Principle
428(1)
Error Correction Rules --- Single Threshold Element
428(6)
Error Correction Rules --- Multi-Element Networks
434(3)
Steepest-Descent Rules --- Single Threshold Element
437(14)
Steepest-Descent Rules --- Multi-Element Networks
451(11)
Summary
462(2)
Bibliography
464(11)
Neural Control Systems
475(20)
A Nonlinear Adaptive Filter Based on Neural Networks
475(1)
A MIMO Nonlinear Adaptive Filter
475(4)
A Cascade of Linear Adaptive Filters
479(1)
A Cascade of Nonlinear Adaptive Filters
479(1)
Nonlinear Inverse Control Systems Based on Neural Networks
480(4)
The Truck Backer-Upper
484(3)
Applications to Steel Making
487(4)
Applications of Neural Networks in the Chemical Process Industry
491(2)
Bibliography
493(2)
Glossary 495(8)
Index 503


Bernard Widrow, PhD, has been Professor of Electrical Engineering at Stanford University for forty years. Together with M.E. Hoff, Jr., Dr. Widrow invented the LMS algorithm, which is now the world's most widely used learning algorithm. He is the recipient of numerous industry awards and holds twenty U.S. or foreign patents. Dr. Widrow has published nearly 200 papers, two of which became Citation Classics.

Eugene Walach, PhD, is Director and Senior Researcher of R&D Management at IBM Haifa (Israel) Research Labs. His research areas include industrial vision-based applications, low-level image analysis, and signal processing. He and Dr. Widrow developed their system of adaptive inverse control.
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