Distributed Control and Filtering for Industrial Systems

Distributed Control and Filtering for Industrial Systems......Page 4
Contents......Page 8
Preface......Page 15
Acknowledgments......Page 16
Notations and symbols......Page 17
List of acronyms......Page 18
1 Introduction......Page 20
1.1 Introduction......Page 21
1.2.1 Decentralized control......Page 23
1.2.2 Interconnected systems......Page 25
1.2.3 Quasidecentralized control......Page 27
1.2.4 Decentralized networked control......Page 28
1.2.5 Distributed control......Page 29
1.2.6 Distributed networked control......Page 30
1.2.7 Distributed control and game theory......Page 31
1.2.8 Networked control systems......Page 32
1.2.9 Wireless networked control system......Page 36
1.2.10 Multiagents control systems......Page 37
1.2.11 Coordinated hierarchical control......Page 38
1.2.12 Hierarchical control of multilayers systems......Page 39
1.3.1 New algorithms with guaranteed properties......Page 42
1.3.6 Band-limited channels......Page 43
1.3.10 DecNCS and DNC stability......Page 44
1.4 Information flow......Page 45
1.5 Performance evaluation......Page 48
1.6 Advantages of distributed control systems......Page 49
1.7.1 Methodology......Page 50
1.7.2 Chapter organization......Page 51
1.8 Notes......Page 52
2.1 Overview......Page 54
2.2 Dissipativity-based approach......Page 55
2.2.1 Control system models......Page 57
2.2.2 Interconnection stability......Page 58
2.2.3 Quadratic dissipativity......Page 61
2.2.4 Interconnection stability condition......Page 62
2.2.5 Semidefinite programming......Page 65
2.2.6 Objective function......Page 66
2.2.9 Stability constraint......Page 67
2.2.10 Receding-horizon stability constraint......Page 68
2.2.12 Robust feasibility......Page 69
2.2.13 Dissipativity matrices......Page 72
2.2.14 Dissipativity-based DMPC algorithm......Page 73
2.2.15 Illustrative example 2.1......Page 74
2.3.1 Introduction......Page 78
2.3.2 Interconnected systems......Page 79
2.3.3 MPC strategy......Page 83
2.3.4 MPC with stability constraint......Page 84
2.3.5 Stability consideration......Page 85
2.4.1 Introduction......Page 87
2.4.2 Problem formulation......Page 90
2.4.3 Stability properties......Page 96
2.4.4 Design procedure......Page 98
2.4.5 Illustrative example 2.2......Page 101
2.4.6 Illustrative example 2.3......Page 103
2.5 Feasible cooperation model predictive control......Page 111
2.5.1 System models......Page 112
2.5.2 Information structures......Page 114
2.5.3 MPC frameworks......Page 116
2.5.4 Terminal penalty method......Page 119
2.5.5 Algorithm and properties......Page 120
2.5.8 Initialization......Page 124
2.5.9 Nominal closed-loop stability......Page 125
2.6 Application to power plant control......Page 128
2.7 Application to quadruple-tank process......Page 130
2.8 Application to automatic generation control......Page 133
2.9 Notes......Page 142
3.1 Introduction......Page 146
3.1.1 Information pattern......Page 147
3.1.2 Suboptimal controller synthesis......Page 150
3.1.3 Stabilizability......Page 151
3.1.4 Numerical algorithms......Page 152
3.1.6 Identifying solvable information patterns......Page 154
3.2 Identical decoupled dynamical systems......Page 155
3.2.1 Introduction......Page 156
3.2.2 LQR properties......Page 158
3.2.3 Distributed control design......Page 164
3.2.4 Finite strings......Page 167
3.2.7 Arbitrary graph structures......Page 169
3.2.8 Measure of suboptimality......Page 171
3.2.9 Illustrative example 3.3......Page 172
3.3.1 Introduction......Page 175
3.3.2 Problem formulation......Page 177
3.3.3 Illustrative example 3.4......Page 178
3.3.4 Illustrative example 3.5......Page 180
3.3.5 Illustrative example 3.6......Page 181
3.3.6 Illustrative example 3.7......Page 182
3.3.7 Control design......Page 188
3.3.8 Illustrative example 3.8......Page 190
3.4 Distributed control of nonnegative systems......Page 191
3.4.1 Illustrative example 3.9......Page 192
3.4.2 Distributed stabilization by linear programming......Page 193
3.4.3 Illustrative example 3.10......Page 194
3.4.4 Illustrative example 3.11......Page 196
3.5.2 Distributed systems......Page 197
3.5.3 Iterative gradient algorithm......Page 199
3.5.4 Power system model......Page 201
3.5.5 State-space representation......Page 203
3.5.6 Simulation study......Page 204
3.6 Notes......Page 207
4.1 Observer-based control......Page 208
4.1.1 Problem definition and system modeling......Page 210
4.1.2 Induced delays......Page 212
4.1.3 Packet dropout......Page 214
4.1.5 Sampling interval selection......Page 216
4.1.6 Transmission constraints......Page 217
4.1.8 Network-induced errors......Page 218
4.1.9 Control design......Page 219
4.1.10 Closed-loop system......Page 220
4.1.11 Stability analysis......Page 221
4.1.12 Illustrative example 4.1......Page 222
4.2.1 Introduction......Page 227
4.2.3 Problem formulation......Page 229
4.2.5 Model-based networked control structure......Page 230
4.2.6 WSN transmission scheduling and model updates......Page 231
4.2.7 Networked closed-loop stability......Page 232
4.3 Application to chemical reactors......Page 233
4.3.3 Simulation results......Page 236
Appendix A: System matrices and data......Page 239
4.4 Notes......Page 241
5.1 Consensus of multiagent systems......Page 242
5.1.1 Dynamic consensus with observer-type protocol......Page 243
5.1.2 Dynamic consensus......Page 244
5.1.3 Consensus region......Page 246
5.1.4 Consensus with neutrally stable matrix......Page 247
5.1.5 Consensus with prescribed convergence speed......Page 248
5.1.6 Illustrative example 5.1......Page 251
5.1.7 Consensus with static protocols......Page 252
5.1.8 Formation control......Page 253
5.1.9 Illustrative example 5.2......Page 254
5.2 Consensus control for time-delay systems......Page 255
5.2.2 Problem formulation......Page 256
5.2.3 Fixed interconnection topology......Page 259
5.2.4 Switched interconnection topology......Page 263
5.2.5 Illustrative example 5.3......Page 266
5.2.6 Illustrative example 5.4......Page 267
5.3.1 Introduction......Page 268
5.3.2 Problem description......Page 269
5.3.3 Analytic results......Page 271
5.3.4 Illustrative example 5.5......Page 279
5.4 Notes......Page 288
6.1 Introduction......Page 290
6.2 Problem formulation......Page 292
6.3 Convergence of the centralized estimation error......Page 294
6.4.1 Distributed variance minimization......Page 296
6.4.2 Optimal weights for variance minimization......Page 299
6.4.3 Bounds on the error variance......Page 300
6.4.4 Distributed computation of constraints......Page 302
6.4.5 Estimation of error covariance......Page 303
6.4.7 Numerical results......Page 306
6.4.8 Estimator structure and implementation......Page 307
6.5 Asynchronous multirate multismart sensors......Page 309
6.5.1 Introduction......Page 310
6.5.2 Problem formulation......Page 314
6.5.4 Dealing with delay......Page 316
6.5.5 Fusion algorithm......Page 319
6.5.6 Single-sensor simulations......Page 321
6.5.7 Multiple sensor simulations......Page 327
6.6 Distributed non-linear estimation......Page 330
6.6.1 Introduction......Page 331
6.6.2 Problem formulation......Page 333
6.6.3 Analytic results......Page 337
6.6.4 Illustrative example 5.1......Page 342
6.7 Notes......Page 347
7.1 Introduction......Page 350
7.2 Self-tuning Kalman filtering......Page 351
7.2.2 Problem formulation......Page 352
7.2.3 Self-tuning distributed Kalman fusion filter......Page 354
7.2.4 Distributed STKFF without feedback......Page 355
7.2.5 Optimality of STKFF with feedback......Page 356
7.2.6 Global optimality of the feedback filtering fusion......Page 357
7.2.7 Evaluation and testing......Page 359
7.3.1 Introduction......Page 361
7.3.2 Problem formulation......Page 362
7.3.4 Modified centralized multisensor system......Page 368
7.3.5 Process data collection......Page 375
7.3.6 Model of the coupled tank system......Page 376
7.3.7 Evaluation of results......Page 379
7.4 Notes......Page 380
8.1 Introduction......Page 382
8.2 Overview of related work......Page 383
8.3 Simulation environments......Page 386
8.4.1 Illustrative example 8.1......Page 388
8.4.2 Illustrative example 8.2......Page 389
8.5 Experimental setup......Page 392
8.6 Multiagent modeling approach......Page 394
8.6.1 Introduction......Page 395
8.6.2 A reference model......Page 396
8.6.3 Organization model......Page 398
8.6.5 Job model......Page 399
8.6.6 Intelligence model......Page 400
8.6.7 Coordination model......Page 401
8.7.1 Introduction......Page 402
8.8.1 CAN network......Page 404
8.8.3 Network impact on feedback control systems......Page 405
8.8.4 Simulation results......Page 406
8.8.6 Information lost......Page 407
8.8.7 Shared network......Page 408
8.9 Design consideration......Page 409
8.10.1 Networked control architectures......Page 412
8.10.2 Cooperative, distributed control architectures......Page 414
8.10.3 A reactor–separator process example......Page 416
8.11 Notes......Page 419
9.1 Notations......Page 420
9.1.1 Kronecker products......Page 421
9.2 Elements of graph theory......Page 422
9.2.2 Laplacian spectrum of graphs......Page 423
9.2.3 Properties of adjacency matrix......Page 424
9.3 Minimum mean square estimate......Page 426
9.4.1 Practical stabilizability......Page 427
9.4.2 Razumikhin stability......Page 428
9.5.1 Schur complements......Page 429
9.5.2 Bounding inequalities......Page 431
9.6.1 Basics......Page 435
9.6.2 Some standard problems......Page 436
9.6.3 The S-procedure......Page 437
9.7.1 Inverse of block matrices......Page 438
9.7.2 Matrix inversion lemma......Page 439
References......Page 440
Index......Page 474