Optimal and Robust Scheduling for Networked Control Systems tackles the problem of integrating system components—controllers, sensors, and actuators—in a networked control system. It is common practice in industry to solve such problems heuristically, because the few theoretical results available are not comprehensive and cannot be readily applied by practitioners. This book offers a solution to the deterministic scheduling problem that is based on rigorous control theoretical tools but also addresses practical implementation issues. Helping to bridge the gap between control theory and computer science, it suggests that the consideration of communication constraints at the design stage will significantly improve the performance of the control system. Technical Results, Design Techniques, and Practical Applications The book brings together well-known measures for robust performance as well as fast stochastic algorithms to assist designers in selecting the best network configuration and guaranteeing the speed of offline optimization. The authors propose a unifying framework for modelling NCSs with time-triggered communication and present technical results. They also introduce design techniques, including for the codesign of a controller and communication sequence and for the robust design of a communication sequence for a given controller. Case studies explore the use of the FlexRay TDMA and time-triggered control area network (CAN) protocols in an automotive control system. Practical Solutions to Your Time-Triggered Communication Problems This unique book develops ready-to-use engineering tools for large-scale control system integration with a focus on robustness and performance. It emphasizes techniques that are directly applicable to time-triggered communication problems in the automotive industry and in avionics, robotics, and automated manufacturing.
Author(s): Stefano Longo, Tingli Su, Guido Herrmann, Phil Barber
Series: Automation and Control Engineering
Publisher: CRC Press
Year: 2013
Language: English
Pages: xxxii+246
Tags: Автоматизация;Теория автоматического управления (ТАУ);Книги на иностранных языках;
Optimal and Robust Scheduling for Networked Control Systems......Page 6
Contents......Page 10
List of Figures......Page 16
List of Tables......Page 18
List of Acronyms......Page 20
Notation and Symbols......Page 22
Preface......Page 26
How to read this book......Page 27
Acknowledgments......Page 28
Author Biographies......Page 30
1 Introduction......Page 34
1.1.2. Networked control systems......Page 35
1.1.3. Limited communication systems......Page 38
1.2. Motivation......Page 39
2 Control of plants with limited communication......Page 42
2.1. Introduction......Page 43
2.2. Practical considerations......Page 45
2.2.1.1. Contention-based paradigms......Page 46
2.2.1.2. Contention-free paradigms......Page 48
2.3.1. Modeling the contention-based paradigm......Page 49
2.3.2.1. The non-zero-order-hold case......Page 51
2.3.2.2. The zero-order-hold case......Page 53
2.3.2.3. The model-based case......Page 55
2.4.1. Stochastic and robust control......Page 57
2.4.2. Estimation......Page 59
2.5. Scheduling methods......Page 61
2.5.1. Open-loop scheduling......Page 62
2.5.2. Closed-loop scheduling......Page 63
2.6. Scheduling and controller codesign methods......Page 65
2.6.1. Offine scheduling and controller codesign......Page 66
2.6.2. Online scheduling and controller codesign......Page 68
2.7.1. Stability with delays and packet dropout......Page 71
2.7.1.2. Variable sampling and delays......Page 72
2.7.1.4. Packet dropout......Page 73
2.7.1.5. Scheduling algorithms......Page 74
2.7.2. Structural properties and stability for the codesign problem......Page 76
2.8. Nonlinear NCSs......Page 78
2.9. Summary......Page 79
3 A general framework for NCS modeling......Page 80
3.1. Introduction......Page 81
3.2.1. A time-varying star graph......Page 82
3.2.2. The scheduler......Page 83
3.3. NCS modeling......Page 86
3.3.1. Augmented plant......Page 91
3.3.2. Augmented controller......Page 93
3.3.3. Augmented closed-loop system......Page 94
3.4. NCS without ZOH......Page 95
3.5. Periodicity and discrete-time lifting......Page 96
3.5.1. Elimination of periodicity via lifting......Page 98
3.6.1. Multi-networks......Page 103
3.6.2. Subnetworks and task scheduling......Page 105
3.7. Multirate systems, a special case of NCSs......Page 110
3.8.1. Switched systems......Page 111
3.8.2. Delayed systems......Page 112
3.9. Application to a vehicle brake-by-wire control system......Page 114
3.9.1. Modeling the bus and task scheduling......Page 116
3.10. Summary......Page 119
4.1. Introduction......Page 120
4.2. NCSs with ZOH......Page 122
4.2.1. Controllability and stabilizability......Page 123
4.3. NCSs without ZOH......Page 133
4.3.1. Stabilizability and detectability......Page 134
4.4. Sampled-data case......Page 137
4.5. Examples......Page 138
4.6. Summary......Page 143
5.1. Introduction......Page 144
5.2. Optimization problem......Page 145
5.2.1. Analysis of properties......Page 146
5.3. Optimization algorithms......Page 151
5.3.1. Genetic Algorithm optimization......Page 152
5.3.2.1. PSO1......Page 153
5.3.2.2. PSO2......Page 154
5.3.3. Discussion on algorithm performance......Page 155
5.4. Constraint handling......Page 156
5.6. Optimization of NCSs which are multirate systems......Page 157
5.6.1. Bus occupancy as a constraint......Page 158
5.7. Applying the optimization to the vehicle brake-by-wire control system......Page 159
5.8. Summary......Page 160
6.1. Introduction......Page 162
6.2.1. NCS model......Page 164
6.2.2. Quadratic cost function......Page 166
6.3. Optimal codesign......Page 170
6.4. Examples......Page 171
6.5. Summary......Page 174
7.1. Introduction......Page 176
7.2.1. NCS model......Page 178
7.2.2. Quadratic cost function......Page 180
7.2.3. A model reference approach for the performance matrix......Page 181
7.3. Optimal design......Page 183
7.4. Examples......Page 185
7.5. Summary......Page 187
8 Robust schedule design......Page 188
8.1. Introduction......Page 189
8.2.1. NCS model......Page 190
8.2.2. Cost function......Page 192
8.3.1.1. Discrete-time lifted controller......Page 195
8.3.1.2. Discrete-time lifted closed-loop system......Page 196
8.4. Formulation of a sampled-data H∞-based cost for robustness and performance......Page 197
8.4.1. Continuous-time lifting......Page 198
8.4.2. NCS model......Page 203
8.4.3. Cost function......Page 205
8.5. Optimal design with an example......Page 206
8.6. Summary......Page 208
9.1. Introduction......Page 210
9.2.1.1. Linearized longitudinal dynamics......Page 211
9.2.1.2. Driveline dynamics......Page 212
9.2.1.3. Air-conditioning system......Page 215
9.2.2. Design of an observer and an LQR controller with integral action......Page 218
9.3. HIL from TTE systems......Page 222
9.3.1. Modifications to the original HIL......Page 224
9.4. Experiments on the HIL......Page 226
9.4.1. Simulation results......Page 227
9.4.2. HIL quadratic performance results......Page 229
9.4.3. HIL robust performance results......Page 231
9.5.1. Brief overview of FlexRay and its development tools......Page 236
9.5.2. Optimal cycle scheduling for FlexRay......Page 238
9.5.3. Results for the FlexRay setup......Page 239
9.6. Summary......Page 241
10.1. Introduction......Page 244
10.3. Sampled-data model of nonlinear NCS......Page 246
10.3.1. Time discretization of a multi-input nonlinear affine system......Page 247
10.3.2. Scheduling of actuator information......Page 248
10.4.1. Cost function for the sampled-data system......Page 249
10.4.2. Removal of periodicity and cost for optimization......Page 250
10.5.1. Generic optimization problem......Page 252
10.5.2. Lyapunov function for the SOS-approach......Page 253
10.6. An SOS-framework for local cost computation......Page 254
10.7. Example......Page 256
10.8. Summary......Page 263
Bibliography......Page 264
Index......Page 276
