A collection of research articles dedicated to the 60th birthday of Prof. Shinji Hara of the University of Tokyo, highlighting papers on control theory and its applications. - suitable for researchers, PhD students and experienced engineers working in the field of control engineering.
Author(s): Li Qiu, Jie Chen, Tetsuya Iwasaki, Hisaya Fujioka
Series: IET Control Engineering Series 76
Publisher: The Institution of Engineering and Technology
Year: 2012
Language: English
Pages: xxiv+200
Developments in Control Theory Towards Glocal Control......Page 4
Contents......Page 8
Preface......Page 14
Selected Publications of Shinji Hara......Page 18
List of Contributors......Page 24
Part I: Robust and Optimal Control......Page 26
1.2 Linear networks......Page 28
1.3 Controller design for an unknown system......Page 30
References......Page 31
2.1 Introduction......Page 32
2.2 Problem statement......Page 33
2.3 Weak separation principle and quantized state estimation......Page 34
2.4 Quantized LQG control for a scalar system......Page 36
References......Page 39
3.1 Introduction......Page 42
3.2 Problem statement......Page 43
3.3 Preliminaries......Page 45
3.4.1 Analysis......Page 46
3.4.2 Design......Page 47
3.5 Numerical example......Page 48
Acknowledgment......Page 49
References......Page 50
4.1 Introduction......Page 52
4.2 Problem statement......Page 53
4.3 Main results......Page 56
4.4 Numerical example......Page 57
References......Page 60
5.1 Introduction......Page 62
5.2 Preliminaries and problem formulation......Page 64
5.3 Stabilizability......Page 66
5.4 Optimal tracking performance......Page 68
References......Page 71
6.2 Problem setup......Page 74
6.3.2 Stability analysis......Page 76
6.4 Numerical example......Page 78
References......Page 79
Part II: Mathematical System and Control Theory......Page 80
7.1 Introduction......Page 82
7.2 The variation of information metric......Page 83
7.3 MMI as an optimization problem......Page 85
7.4 A greedy algorithm for MMI in the n×m case......Page 86
7.5 All optimal reduced-order approximations are aggregations......Page 87
7.6 Finding an optimal aggregation: a reformulation......Page 88
References......Page 89
8.1 Introduction......Page 92
8.2.1 Coprimeness......Page 93
8.3 On compact sets in the graph topology......Page 95
8.3.1 MIMO case......Page 98
8.4.1 Approximation in sampled-data systems......Page 99
References......Page 101
9.2 Frequency domain system identification......Page 104
9.3.1 Subspace identification......Page 107
9.3.2 Matrix pencil approach......Page 108
9.3.2.1 Zero initial conditions......Page 110
9.3.2.2 Example......Page 111
References......Page 112
10.1 Introduction......Page 114
10.2 Data-based PWA map......Page 116
10.3 Problem description......Page 117
10.4.1 Measure of model complexity......Page 118
10.4.2 Reduction to optimization problem......Page 119
10.5 Experiment with a DC motor system......Page 120
References......Page 123
11.1 Introduction......Page 124
11.2 Background on the inerter......Page 125
11.3 Two-axle railway vehicles models and track inputs......Page 126
11.5 Performance benefits of minimizing the vertical body acceleration J1......Page 127
Acknowledgment......Page 131
References......Page 132
12.1 Introduction......Page 134
12.2 Formulation......Page 135
12.3 Main result......Page 139
12.4 Numerical example......Page 140
12.5 Conclusion......Page 141
References......Page 142
Part III: Networked Dynamical Systems and Glocal Control......Page 144
13.1 Introduction......Page 146
13.2.1 Notation......Page 147
13.2.2 Shape control......Page 148
13.2.3 Flocking behavior......Page 149
13.3.1 Undirected consensus graph......Page 150
13.3.3 Directed consensus graph......Page 151
13.5 Conclusions......Page 153
References......Page 155
14.1.1 Pervasive networked sensing......Page 156
14.1.3 Summary of results......Page 157
14.2.1 System setup......Page 158
14.2.2 Battery modeling......Page 159
14.2.5 Energy state estimation and certainty equivalence......Page 160
14.3 Main results: no random failures......Page 161
14.4 Main results: random failures......Page 163
References......Page 169
15.1 Introduction......Page 172
15.2 The PageRank problem......Page 173
15.3 Distributed algorithm under Markovian communication......Page 174
15.4 Convergence properties of the distributed algorithm......Page 177
References......Page 180
16.1 Introduction......Page 182
16.2 Problem formulation......Page 184
16.3 Preliminary on H2 optimal control......Page 187
16.4 Main result......Page 190
16.5 An illustrative example......Page 193
References......Page 194
17.1 Introduction......Page 198
17.2.1 Problem formulation......Page 199
17.2.2 Cluster reducibility......Page 201
17.2.3 Numerical examples......Page 203
17.3 Toward hierarchical distributed observer......Page 205
References......Page 206
18.1 Introduction......Page 208
18.2.1 Problem statement......Page 209
18.2.2 Multivariable harmonic balance......Page 211
18.3.1 The MHB condition......Page 212
18.3.2 Stability analysis and existence of oscillations......Page 214
References......Page 216
In Memory of Hisaya Fujioka......Page 218
Index......Page 220
