Networked Embedded Sensing and Control: Workshop NESC’05: University of Notre Dame, USA, October 2005 Proceedings

Contents......Page 8
0......Page 17
1 Robotic Networks and Complexity......Page 18
2 Example Algorithms and Tasks......Page 19
Aggregation algorithms......Page 20
Deployment algorithms......Page 21
1 Introduction......Page 23
2 Oscillator Models for Multivehicle Systems......Page 24
3 The Discrete Time Kuramoto Model......Page 25
3.1 The Two Oscillator Problem......Page 26
3.2 The N-Oscillator Problem......Page 29
4 Discrete Time Kuramoto Model with Time Delay......Page 32
5 Simulation Results......Page 34
6 Conclusions and Directions of Future Work......Page 35
1 Introduction......Page 38
2 Problem formulation......Page 40
3 Stochastic and doubly stochastic matrices......Page 43
4 Symmetric Controllers......Page 45
4.1 Cayley Stochastic Matrices on Finite Abelian Groups......Page 46
4.2 The Spectral Radius for Stochastic Cayley Matrices......Page 49
5.1 Random Circulant Communication Graph......Page 51
5.2 Random Communication Graph with Bounded In-Degree......Page 52
5.3 Convergence and Performance Analysis......Page 53
7 Conclusions......Page 55
A Proof of Theorem 1......Page 57
B Proof of Proposition 1.......Page 59
C Proof of Proposition 2.......Page 61
D Proof of Proposition 3.......Page 63
1 Introduction......Page 65
2.1 De.nitions and Notations......Page 67
2.2 Synchronous and Asynchronous Consensus Protocols......Page 68
2.3 Other Communication Assumptions......Page 70
2.4 A Classi.cation of Consensus Results......Page 71
3 Bidirectional Versus Unidirectional Communication......Page 72
4 Iteration Graph and Nonlinear Asynchronous Consensus Protocols......Page 74
4.1 Iteration Graph......Page 75
4.2 Nonlinear Asynchronous Consensus Protocols......Page 77
5 Conclusions......Page 79
1 Introduction......Page 80
2 Formations, Connectivity Graphs, and Feasibility......Page 81
3.1 Feasible Connectivity Graph Transitions......Page 83
3.2 Reachability and Connectivity Graph Processes......Page 84
3.3 Global Objectives, Desirable Transitions and Optimality......Page 86
4.1 Production of Low-Complexity Formations......Page 87
4.2 Collaborative Beamforming in Sensor Networks......Page 88
4.3 Other Applications......Page 92
Conclusions......Page 93
5......Page 94
1 Introduction......Page 96
2.2 Networked Control Simulation......Page 98
3 GTSNetS Overview......Page 99
4 Extension of GTSNetS to Simulate Distributed Control Using Sensor Networks......Page 101
5 Fault Tolerance Algorithms......Page 104
5.1 Distributed Bayesian Algorithm......Page 105
5.2 Simulation of the Bayesian Solution Using GTSNetS......Page 106
5.3 Enhancement of the Bayesian Fault Tolerance Algorithm......Page 107
6 Hierarchical Distributed Control with Fault Tolerance......Page 111
7 Conclusion......Page 114
1 Introduction......Page 116
2 Problem Description......Page 119
3.1 build utilities......Page 120
3.2 SOS, and sos utilities......Page 121
3.3 necroot......Page 122
3.4 FreeMat utilities......Page 126
4 Conclusion......Page 129
Appendix A......Page 131
1 Introduction......Page 135
2 A Categorical Formulation of Tagged Systems......Page 138
3 Universal Heterogeneous Composition......Page 142
4 Equivalent Deployments of Tagged Systems......Page 146
5 Networks of Tagged Systems......Page 150
6 Universally Composing Networks of Tagged Systems......Page 155
7 Semantics Preserving Deployments of Networks......Page 159
8 Conclusion......Page 162
1 Introduction......Page 163
2 Kalman Filter: Information Form......Page 164
3 Distributed Kalman Filter and Micro-KFs......Page 166
4 Consensus Filters......Page 168
5 Simulation Results......Page 170
6 Conclusions......Page 171
1 Introduction......Page 174
2 Belief Consensus on Bayesian Networks......Page 176
3 Lyapunov Function for Belief Consensus......Page 180
4 Distributed Detection of Multi-target Formations......Page 181
4.2 Feature Vectors for Multi-target Formation Detection......Page 182
4.4 Multi-target Formation Detection......Page 183
6 Conclusions......Page 185
1 Introduction......Page 188
2.1 Notation......Page 190
3 Evidence Filtering......Page 192
3.2 Spatio-temporal Evidence Filtering......Page 194
4.1 FIR Evidence Filters......Page 196
5 Low Signature Threat Detection......Page 199
6 Conclusion......Page 201
12......Page 204
1 Introduction......Page 206
2 Network Control Systems Modeling......Page 209
2.1 Non-anticipative Control Unit......Page 210
2.2 Anticipative Control Unit......Page 211
3 Stability of Delay Di.erential Equations......Page 212
4 Observer-Based Controller Design for NCSs......Page 214
5 Conclusions and Future Work......Page 219
Appendix......Page 220
1 Introduction......Page 222
2 Notation and Preliminaries......Page 224
3 Main Result......Page 226
4 Discussion......Page 227
5 Conclusions......Page 231
1 Introduction......Page 233
2 Continuous Lifting Technique......Page 237
3 An H2 Norm Characterization of a MB-NCS......Page 238
4 A Generalized H2 Norm for MB-NCS......Page 241
5 Optimal Controllers for MB-NCS......Page 246
6 Conclusions......Page 250
1 Introduction......Page 252
2.1 Model Description......Page 253
2.1.1. Quantization (Data Word) Model......Page 254
2.1.2. System model......Page 255
2.2.2. Lower bound on constant stabilizing data rate......Page 256
3 Theoretical Results......Page 257
3.1.2 SISO Controllable Canonical System......Page 258
3.2 Relationship to Bounds in Literature......Page 259
3.2.1 Systems with Unstable ”Butterworth Type” Eigenvalue Distribution......Page 260
3.2.4 Robustness with Respect to System Coe.cients......Page 261
4 Beating the Bounds: Possibility, Implementation, and Simulations......Page 262
4.1 Exponent Compression/Encoding Scheme......Page 263
4.3 Implementation of Simulation......Page 264
4.4 Possibility of Beating the Theoretical Bound......Page 265
4.5 Simulation Results......Page 266
5 Conclusion......Page 268
1 Introduction......Page 270
1.1 Problem Formulation......Page 271
1.3 Notation......Page 272
1.4 Basic De.nitions of Information Theory......Page 273
1.6 Elementary Properties of h and I .......Page 275
2 Technical Framework and Assumptions......Page 276
3 Motivation Case Study: A Remote Preview System Based on an Additive White Gaussian Channel......Page 278
3.2 Optimal Design of a Linear and Time-Invariant Remote Preview Control Scheme......Page 279
3.3 First Step: Design of E and D......Page 280
3.4 Optimal Scheme in the Presence of a Linear Feedback Controller......Page 282
3.5 Motivation for Studying the General Case......Page 283
4.1 Derivation of a General Bound Involving Entropy Rates......Page 284
4.2 Expressing Performance Limitations by Means of Asymptotic Power Spectral Densities: An Extension to Bode’s Integral Formula......Page 285
5 Conclusions......Page 286
18......Page 287
19......Page 288
1 Introduction......Page 289
1.2 Our Contributions......Page 291
2 System Model......Page 293
3.1 m -Phase TDMA......Page 294
3.2 Slotted ALOHA......Page 297
4 Simulation Results......Page 298
4.1 m -Phase TDMA Network......Page 299
4.2 Slotted ALOHA......Page 301
4.3 Comparison......Page 303
5 Conclusions......Page 304
20......Page 306
1 Introduction......Page 307
2 A Routing Scheme with Energy-Balancing Guarantees......Page 308
3 Performance Analysis......Page 314
4 Hardware Implementation......Page 316
5 Closing Remarks......Page 321
1 Introduction......Page 322
2 The Case of Two Interconnected Subsystems......Page 324
2.1 Distributed analysis......Page 325
2.2 Distributed controller synthesis......Page 328
3 General Multi-link Interconnections......Page 331
4 A Power Network Example......Page 335
5 Conclusion and Future Work......Page 339
References......Page 340