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Cover image for Adaptive inverse control : a signal processing approach
Title:
Adaptive inverse control : a signal processing approach
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Edition:
Reissue ed.
Publication Information:
Hoboken, NJ : Wiley-Interscience, 2008
ISBN:
9780470226094
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30000010160611 TJ217 W52 2008 Open Access Book Book
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Summary

Summary

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.


Author Notes

Eugene Walach is Director and Senior Researcher of R&D Management at IBM Haifa (Israel) Research Labs.


Table of Contents

Prefacep. xv
1 The Adaptive Inverse Control Conceptp. 1
1.0 Introductionp. 1
1.1 Inverse Controlp. 2
1.2 Sample Applications of Adaptive Inverse Controlp. 7
1.3 An Outline or Road Map for This Bookp. 22
Bibliographyp. 33
2 Wiener Filtersp. 40
2.0 Introductionp. 40
2.1 Digital Filters, Correlation Functions, z-Transformsp. 40
2.2 Two-Sided (Unconstrained) Wiener Filtersp. 45
2.3 Shannon-Bode Realization of Causal Wiener Filtersp. 51
2.4 Summaryp. 57
Bibliographyp. 57
3 Adaptive LMS Filtersp. 59
3.0 Introductionp. 59
3.1 An Adaptive Filterp. 60
3.2 The Performance Surfacep. 61
3.3 The Gradient and the Wiener Solutionp. 62
3.4 The Method of Steepest Descentp. 64
3.5 The LMS Algorithmp. 65
3.6 The Learning Curve and Its Time Constantsp. 67
3.7 Gradient and Weight-Vector Noisep. 67
3.8 Misadjustment Due to Gradient Noisep. 69
3.9 A Design Example: Choosing Number of Filter Weights for an Adaptive Predictorp. 71
3.10 The Efficiency of Adaptive Algorithmsp. 74
3.11 Adaptive Noise Canceling: A Practical Application for Adaptive Filteringp. 77
3.12 Summaryp. 81
Bibliographyp. 84
4 Adaptive Modelingp. 88
4.0 Introductionp. 88
4.1 Idealized Modeling Performancep. 90
4.2 Mismatch Due to Use of FIR Modelsp. 91
4.3 Mismatch Due to Inadequacies in the Input Signal Statistics; Use of Dither Signalsp. 93
4.4 Adaptive Modeling Simulationsp. 97
4.5 Summaryp. 102
Bibliographyp. 108
5 Inverse Plant Modelingp. 111
5.0 Introductionp. 111
5.1 Inverses of Minimum-Phase Plantsp. 111
5.2 Inverses of Nonminimum-Phase Plantsp. 113
5.3 Model-Reference Inversesp. 117
5.4 Inverses of Plants with Disturbancesp. 120
5.5 Effects of Modeling Signal Characteristics on the Inverse Solutionp. 126
5.6 Inverse Modeling Errorp. 126
5.7 Control System Error Due to Inverse Modeling Errorp. 128
5.8 A Computer Simulationp. 130
5.9 Examples of Offline Inverse Modeling of Nonminimum-Phase Plantsp. 131
5.10 Summaryp. 136
6 Adaptive Inverse Controlp. 138
6.0 Introductionp. 138
6.1 Analysisp. 141
6.2 Computer Simulation of an Adaptive Inverse Control Systemp. 144
6.3 Simulated Inverse Control Examplesp. 147
6.4 Application to Real-Time Blood Pressure Controlp. 154
6.5 Summaryp. 159
Bibliographyp. 159
7 Other Configurations for Adaptive Inverse Controlp. 160
7.0 Introductionp. 160
7.1 The Filtered-X LMS Algorithmp. 160
7.2 The Filtered-[epsilon] LMS Algorithmp. 165
7.3 Analysis of Stability, Rate of Convergence, and Noise in the Weights for the Filtered-[epsilon] LMS Algorithmp. 170
7.4 Simulation of an Adaptive Inverse Control System Based on the Filtered-[epsilon] LMS Algorithmp. 175
7.5 Evaluation and Simulation of the Filtered-X LMS Algorithmp. 180
7.6 A Practical Example: Adaptive Inverse Control for Noise-Canceling Earphonesp. 183
7.7 An Example of Filtered-X Inverse Control of a Minimum-Phase Plantp. 186
7.8 Some Problems in Doing Inverse Control with the Filtered-X LMS Algorithmp. 188
7.9 Inverse Control with the Filtered-X Algorithm Based on DCT/LMSp. 194
7.10 Inverse Control with the Filtered-[epsilon] Algorithm Based on DCT/LMSp. 197
7.11 Summaryp. 201
Bibliographyp. 208
8 Plant Disturbance Cancelingp. 209
8.0 Introductionp. 209
8.1 The Functioning of the Adaptive Plant Disturbance Cancelerp. 211
8.2 Proof of Optimality for the Adaptive Plant Disturbance Cancelerp. 212
8.3 Power of Uncanceled Plant Disturbancep. 215
8.4 Offline Computation of Q[subscript k](z)p. 215
8.5 Simultaneous Plant Modeling and Plant Disturbance Cancelingp. 216
8.6 Heuristic Analysis of Stability of a Plant Modeling and Disturbance Canceling Systemp. 223
8.7 Analysis of Plant Modeling and Disturbance Canceling System Performancep. 226
8.8 Computer Simulation of Plant Modeling and Disturbance Canceling Systemp. 229
8.9 Application to Aircraft Vibrational Controlp. 234
8.10 Application to Earphone Noise Suppressionp. 236
8.11 Canceling Plant Disturbance for a Stabilized Minimum-Phase Plantp. 237
8.12 Comments Regarding the Offline Process for Finding Q(z)p. 248
8.13 Canceling Plant Disturbance for a Stabilized Nonminimum-Phase Plantp. 249
8.14 Insensitivity of Performance of Adaptive Disturbance Canceler to Design of Feedback Stabilizationp. 254
8.15 Summaryp. 255
9 System Integrationp. 258
9.0 Introductionp. 258
9.1 Output Error and Speed of Convergencep. 258
9.2 Simulation of an Adaptive Inverse Control Systemp. 261
9.3 Simulation of Adaptive Inverse Control Systems for Minimum-Phase and Nonminimum-Phase Plantsp. 266
9.4 Summaryp. 268
10 Multiple-Input Multiple-Output (MIMO) Adaptive Inverse Control Systemsp. 270
10.0 Introductionp. 270
10.1 Representation and Analysis of MIMO Systemsp. 270
10.2 Adaptive Modeling of MIMO Systemsp. 274
10.3 Adaptive Inverse Control for MIMO Systemsp. 285
10.4 Plant Disturbance Canceling in MIMO Systemsp. 290
10.5 System Integration for Control of the MIMO Plantp. 292
10.6 A MIMO Control and Signal Processing Examplep. 296
10.7 Summaryp. 301
11 Nonlinear Adaptive Inverse Controlp. 303
11.0 Introductionp. 303
11.1 Nonlinear Adaptive Filtersp. 303
11.2 Modeling a Nonlinear Plantp. 307
11.3 Nonlinear Adaptive Inverse Controlp. 311
11.4 Nonlinear Plant Disturbance Cancelingp. 319
11.5 An Integrated Nonlinear MIMO Inverse Control System Incorporating Plant Disturbance Cancelingp. 321
11.6 Experiments with Adaptive Nonlinear Plant Modelingp. 323
11.7 Summaryp. 326
Bibliographyp. 329
12 Pleasant Surprisesp. 330
A Stability and Misadjustment of the LMS Adaptive Filterp. 339
A.1 Time Constants and Stability of the Mean of the Weight Vectorp. 339
A.2 Convergence of the Variance of the Weight Vector and Analysis of Misadjustmentp. 342
A.3 A Simplified Heuristic Derivation of Misadjustment and Stability Conditionsp. 346
Bibliographyp. 347
B Comparative Analyses of Dither Modeling Schemes A, B, and Cp. 349
B.1 Analysis of Scheme Ap. 350
B.2 Analysis of Scheme Bp. 351
B.3 Analysis of Scheme Cp. 352
B.4 A Simplified Heuristic Derivation of Misadjustment and Stability Conditions for Scheme Cp. 356
B.5 A Simulation of a Plant Modeling Process Based on Scheme Cp. 358
B.6 Summaryp. 359
Bibliographyp. 362
C A Comparison of the Self-Tuning Regulator of Astrom and Wittemnark with the Techniques of Adaptive Inverse Controlp. 363
C.1 Designing a Self-Tuning Regulator to Behave like an Adaptive Inverse Control Systemp. 364
C.2 Some Examplesp. 366
C.3 Summaryp. 367
Bibliographyp. 368
D Adaptive Inverse Control for Unstable Linear SISO Plantsp. 369
D.1 Dynamic Control of Stabilized Plantp. 370
D.2 Adaptive Disturbance Canceling for the Stabilized Plantp. 372
D.3 A Simulation Study of Plant Disturbance Canceling: An Unstable Plant with Stabilization Feedbackp. 378
D.4 Stabilization in Systems Having Both Discrete and Continuous Partsp. 382
D.5 Summaryp. 382
E Orthogonalizing Adaptive Algorithms: RLS, DFT/LMS, and DCT/LMSp. 383
E.1 The Recursive Least Squares Algorithm (RLS)p. 384
E.2 The DFT/LMS and DCT/LMS Algorithmsp. 386
Bibliographyp. 394
F A MIMO Application: An Adaptive Noise-Canceling System Used for Beam Control at the Stanford Linear Accelerator Centerp. 396
F.1 Introductionp. 396
F.2 A General Description of the Acceleratorp. 396
F.3 Trajectory Controlp. 399
F.4 Steering Feedbackp. 400
F.5 Addition of a MIMO Adaptive Noise Canceler to Fast Feedbackp. 402
F.6 Adaptive Calculationp. 404
F.7 Experience on the Real Acceleratorp. 406
F.8 Acknowledgementsp. 407
Bibliographyp. 407
G Thirty Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagationp. 409
G.1 Introductionp. 409
G.2 Fundamental Conceptsp. 412
G.3 Adaptation - The Minimal Disturbance Principlep. 428
G.4 Error Correction Rules - Single Threshold Elementp. 428
G.5 Error Correction Rules - Multi-Element Networksp. 434
G.6 Steepest-Descent Rules - Single Threshold Elementp. 437
G.7 Steepest-Descent Rules - Multi-Element Networksp. 451
G.8 Summaryp. 462
Bibliographyp. 464
H Neural Control Systemsp. 475
H.1 A Nonlinear Adaptive Filter Based on Neural Networksp. 475
H.2 A MIMO Nonlinear Adaptive Filterp. 475
H.3 A Cascade of Linear Adaptive Filtersp. 479
H.4 A Cascade of Nonlinear Adaptive Filtersp. 479
H.5 Nonlinear Inverse Control Systems Based on Neural Networksp. 480
H.6 The Truck Backer-Upperp. 484
H.7 Applications to Steel Makingp. 487
H.8 Applications of Neural Networks in the Chemical Process Industryp. 491
Bibliographyp. 493
Glossaryp. 495
Indexp. 503
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