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(s): Widrow Bernard, Walach Eugene
Edition: reissue edition
Year: 2007
Language: English
Pages: 508
Adaptive Inverse Control: A Signal Processing Approach......Page 5
Contents......Page 11
Preface......Page 17
1.0 Introduction......Page 19
1.1 Inverse Control......Page 20
1.2 Sample Applications of Adaptive Inverse Control......Page 25
1.3 An Outline or Road Map for This Book......Page 40
Bibliography......Page 51
2.1 Digital Filters, Correlation Functions, z-Transforms......Page 58
2.2 Two-sided (Unconstrained) Wiener Filters......Page 63
2.3 Shannon-Bode Realization of Causal Wiener Filters......Page 69
2.4 Summary......Page 75
Bibliography......Page 76
3.0 Introduction......Page 77
3.1 An Adaptive Filter......Page 78
3.2 The Performance Surface......Page 79
3.3 The Gradient and the Wiener Solution......Page 80
3.4 The Method of Steepest Descent......Page 82
3.5 The LMS Algorithm......Page 83
3.7 Gradient and Weight-Vector Noise......Page 85
3.8 Misadjustment Due to Gradient Noise......Page 87
3.9 A Design Example: Choosing Number of Filter Weights for an Adaptive Predictor......Page 89
3.10 The Efficiency of Adaptive Algorithms......Page 92
3.11 Adaptive Noise Canceling: A Practical Application for Adaptive Filtering......Page 95
3.12 Summary......Page 99
Bibliography......Page 102
4.0 Introduction......Page 106
4.1 Idealized Modeling Performance......Page 108
4.2 Mismatch Due to Use of FIR Models......Page 109
4.3 Mismatch Due to Inadequacies in the Input Signal Statistics; Use of Dither Signals......Page 111
4.4 Adaptive Modeling Simulations......Page 115
4.5 Summary......Page 120
Bibliography......Page 126
5.1 Inverses of Minimum-Phase Plants......Page 129
5.2 Inverses of Nonminimum-Phase Plants......Page 131
5.3 Model-Reference Inverses......Page 135
5.4 Inverses of Plants with Disturbances......Page 138
5.6 Inverse Modeling Error......Page 144
5.7 Control System Error Due to Inverse Modeling Error......Page 146
5.8 A Computer Simulation......Page 148
5.9 Examples of Offline Inverse Modeling of Nonminimum-Phase Plants......Page 149
5.10 Summary......Page 154
6.0 Introduction......Page 156
6.1 Analysis......Page 159
6.2 Computer Simulation of an Adaptive Inverse Control System......Page 162
6.3 Simulated Inverse Control Examples......Page 165
6.4 Application to Real-Time Blood Pressure Control......Page 172
Bibliography......Page 177
7.1 The Filtered-X LMS Algorithm......Page 178
7.2 The Filtered-ε LMS Algorithm......Page 183
7.3 Analysis of Stability, Rate of Convergence, and Noise in the Weights for the Filtered-e LMS Algorithm......Page 188
7.4 Simulation of an Adaptive Inverse Control System Based on the Filtered-ε LMS Algorithm......Page 193
7.5 Evaluation and Simulation of the Filtered-X LMS Algorithm......Page 198
7.6 A Practical Example: Adaptive Inverse Control for Noise-Canceling Earphones......Page 201
7.7 An Example of Filtered-X Inverse Control of a Minimum-Phase Plant......Page 204
7.8 Some Problems in Doing Inverse Control with the Filtered-X LMS Algorithm......Page 206
7.9 Inverse Control with the Filtered-X Algorithm Based on DCT/LMS......Page 212
7.10 Inverse Control with the Filtered-ε Algorithm Based on DCT/LMS......Page 215
7.11 Summary......Page 219
Bibliography......Page 226
8.0 Introduction......Page 227
8.1 The Functioning of the Adaptive Plant Disturbance Canceler......Page 229
8.2 Proof of Optimality for the Adaptive Plant Disturbance Canceler......Page 230
8.4 Offline Computation of Qk(z)......Page 233
8.5 Simultaneous Plant Modeling and Plant Disturbance Canceling......Page 234
8.6 Heuristic Analysis of Stability of a Plant Modeling and Disturbance Canceling System......Page 241
8.7 Analysis of Plant Modeling and Disturbance Canceling System Performance......Page 244
8.8 Computer Simulation of Plant Modeling and Disturbance Canceling System......Page 247
8.9 Application to Aircraft Vibrational Control......Page 252
8.10 Application to Earphone Noise Suppression......Page 254
8.11 Canceling Plant Disturbance for a Stabilized Minimum-Phase Plant......Page 255
8.12 Comments Regarding the Offline Process for Finding Q(z)......Page 266
8.13 Canceling Plant Disturbance for a Stabilized Nonminimum-Phase Plant......Page 267
8.14 Insensitivity of Performance of Adaptive Disturbance Canceler to Design of Feedback Stabilization......Page 272
8.15 Summary......Page 273
9.1 Output Error and Speed of Convergence......Page 276
9.2 Simulation of an Adaptive Inverse Control System......Page 279
9.3 Simulation of Adaptive Inverse Control Systems for Minimum-Phase and Nonminimum-Phase Plants......Page 284
9.4 Summary......Page 286
10.1 Representation and Analysis of MIMO Systems......Page 288
10.2 Adaptive Modeling of MIMO Systems......Page 292
10.3 Adaptive Inverse Control for MIMO Systems......Page 303
10.4 Plant Disturbance Canceling in MIMO Systems......Page 308
10.5 System Integration for Control of the MIMO Plant......Page 310
10.6 A MIMO Control and Signal Processing Example......Page 314
10.7 Summary......Page 319
11.1 Nonlinear Adaptive Filters......Page 321
11.2 Modeling a Nonlinear Plant......Page 325
11.3 Nonlinear Adaptive Inverse Control......Page 329
11.4 Nonlinear Plant Disturbance Canceling......Page 337
11.5 An Integrated Nonlinear MIMO Inverse Control System Incorporating Plant Disturbance Canceling......Page 339
11.6 Experiments with Adaptive Nonlinear Plant Modeling......Page 341
11.7 Summary......Page 344
Bibliography......Page 347
12 Pleasant Surprises......Page 348
A.1 Time Constants and Stability of the Mean of the Weight Vector......Page 357
A.2 Convergence of the Variance of the Weight Vector and Analysis of Misadjustment......Page 360
A.3 A Simplified Heuristic Derivation of Misadjustment and Stability Conditions......Page 364
Bibliography......Page 365
B Comparative Analyses of Dither Modeling Schemes A, B, and C......Page 367
B.1 Analysis of Scheme A......Page 368
B.2 Analysis of Scheme B......Page 369
B.3 Analysis of Scheme C......Page 370
B.4 A Simplified Heuristic Derivation of Misadjustment and Stability Conditions for Scheme C......Page 374
B.5 A Simulation of a Plant Modeling Process Based on Scheme C......Page 376
B.6 Summary......Page 377
Bibliography......Page 380
C A Comparison of the Self-Tuning Regulator of Åström and Wittenmark with the Techniques of Adaptive Inverse Control......Page 381
C.1 Designing a Self-Tuning Regulator to Behave like an Adaptive Inverse Control System......Page 382
C.2 Some Examples......Page 384
C.3 Summary......Page 385
Bibliography......Page 386
D Adaptive Inverse Control for Unstable Linear SISO Plants......Page 387
D.1 Dynamic Control of Stabilized Plant......Page 388
D.2 Adaptive Disturbance Canceling for the Stabilized Plant......Page 390
D.3 A Simulation Study of Plant Disturbance Canceling: An Unstable Plant with Stabilization Feedback......Page 396
D.5 Summary......Page 400
E Orthogonalizing Adaptive Algorithms: RLS, DFT/LMS, and DCT/LMS......Page 401
E.1 The Recursive Least Squares Algorithm (RLS)......Page 402
E.2 The DRT/LMS and DCT/LMS Algorithms......Page 404
Bibliography......Page 412
F.2 A General Description of the Accelerator......Page 414
F.3 Trajectory Control......Page 417
F.4 Steering Feedback......Page 418
F.5 Addition of a MIMO Adaptive Noise Canceler to Fast Feedback......Page 420
F.6 Adaptive Calculation......Page 422
F.7 Experience on the Real Accelerator......Page 424
Bibliography......Page 425
G.1 Introduction......Page 427
G.2 Fundamental Concepts......Page 430
G.4 Error Correction Rules — Single Threshold Element......Page 446
G.5 Error Correction Rules — Multi-Element Networks......Page 452
G.6 Steepest-Descent Rules — Single Threshold Element......Page 455
G.7 Steepest-Descent Rules — Multi-Element Networks......Page 469
G.8 Summary......Page 480
Bibliography......Page 482
H.2 A MIMO Nonlinear Adaptive Filter......Page 493
H.4 A Cascade of Nonlinear Adaptive Filters......Page 497
H.5 Nonlinear Inverse Control Systems Based on Neural Networks......Page 498
H.6 The Truck Backer-Upper......Page 502
H.7 Applications to Steel Making......Page 505
H.8 Applications of Neural Networks in the Chemical Process Industry......Page 509
Bibliography......Page 511
Glossary......Page 513
Index......Page 521
