UID:
almahu_9948026055202882
Umfang:
1 online resource (498 pages)
Ausgabe:
First edition.
ISBN:
0-12-809221-1
,
0-12-805311-9
Inhalt:
Estimation and Control of Large Scale Networked Systems is the first book that systematically summarizes results on large-scale networked systems. In addition, the book also summarizes the most recent results on structure identification of a networked system, attack identification and prevention. Readers will find the necessary mathematical knowledge for studying large-scale networked systems, as well as a systematic description of the current status of this field, the features of these systems, difficulties in dealing with state estimation and controller design, and major achievements. Numerical examples in chapters provide strong application backgrounds and/or are abstracted from actual engineering problems, such as gene regulation networks and electricity power systems. This book is an ideal resource for researchers in the field of systems and control engineering. Provides necessary mathematical knowledge for studying large scale networked systems Introduces new features for filter and control design of networked control systems Summarizes the most recent results on structural identification of a networked system, attack identification and prevention
Anmerkung:
Front Cover -- Estimation and Control of Large-Scale Networked Systems -- Copyright -- Contents -- Preface -- Acknowledgments -- Notation and Symbols -- 1 Introduction -- 1.1 A General View on Control System Design -- 1.2 Communication and Control -- 1.3 Book Contents -- 1.3.1 Controllability and Observability of a Control System -- 1.3.2 Centralized and Distributed State Estimations -- 1.3.3 State Estimations and Control With Imperfect Communications -- 1.3.4 Veri cation of Stability and Robust Stability -- 1.3.5 Distributed Controller Design for an LSS -- 1.3.6 Structure Identi cation for an LSS -- 1.3.7 Attack Estimation/Identi cation and Other Issues -- 1.4 Bibliographic Notes -- References -- 2 Background Mathematical Results -- 2.1 Linear Space and Linear Algebra -- 2.1.1 Vector and Matrix Norms -- 2.1.2 Hamiltonian Matrices and Distance Among Positive De nite Matrices -- 2.2 Generalized Inverse of a Matrix -- 2.3 Some Useful Transformations -- 2.4 Set Function and Submodularity -- 2.5 Probability and Random Process -- 2.6 Markov Process and Semi-Markov Process -- 2.7 Bibliographic Notes -- References -- 3 Controllability and Observability of an LSS -- 3.1 Introduction -- 3.2 Controllability and Observability of an LTI System -- 3.2.1 Minimal Number of Inputs/Outputs Guaranteeing Controllability/Observability -- 3.2.2 A Parameterization of Desirable Input/Output Matrices -- 3.2.3 Some Nitpicking -- 3.3 A General Model for an LSS -- 3.4 Controllability and Observability for an LSS -- 3.4.1 Subsystem Transmission Zeros and Observability of an LSS -- 3.4.2 Observability Veri cation -- 3.4.3 A Condition for Controllability and Its Veri cation -- 3.4.4 In/Out-degree and Controllability/Observability of a Networked System -- 3.5 Construction of Controllable/Observable Networked Systems -- 3.6 Bibliographic Notes -- Appendix 3.A.
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3.A.1 Proof of Theorem 3.4 -- 3.A.2 Proof of Theorem 3.8 -- 3.A.3 Proof of Theorem 3.9 -- 3.A.4 Proof of Theorem 3.10 -- References -- 4 Kalman Filtering and Robust Estimation -- 4.1 Introduction -- 4.2 State Estimation and Observer Design -- 4.3 Kalman Filter as a Maximum Likelihood Estimator -- 4.3.1 Derivation of the Kalman Filter -- 4.3.2 Convergence Property of the Kalman Filter -- 4.4 Recursive Robust State Estimation Through Sensitivity Penalization -- 4.4.1 Estimation Algorithm -- 4.4.2 Derivation of the Robust Estimator -- 4.4.3 Asymptotic Properties of the Robust State Estimator -- 4.4.4 Boundedness of Estimation Errors -- 4.5 Bibliographic Notes -- Appendix 4.A -- 4.A.1 Proof of Theorem 4.1 -- 4.A.2 Proof of Theorem 4.3 -- References -- 5 State Estimation With Random Data Droppings -- 5.1 Introduction -- 5.2 Intermittent Kalman Filtering (IKF) -- 5.2.1 The IKF Algorithm -- 5.2.2 Mean Square Stability of the IKF -- 5.2.3 Weak Convergence of the IKF -- 5.3 IKF With Switching Sensors -- 5.3.1 Mean Square Stability -- 5.3.2 Second-Order Systems -- 5.3.3 Extension to Higher-Order Systems -- 5.4 IKF With Coded Measurement Transmission -- 5.4.1 Linear Temporal Coding -- 5.4.2 The MMSE Filter -- 5.4.3 Mean Square Stability -- 5.5 Robust State Estimation With Random Data Droppings -- 5.5.1 System With Parametric Errors -- 5.5.2 Robust State Estimator -- 5.5.3 Convergence of the Robust State Estimator -- 5.6 Asymptotic Properties of State Estimations With Random Data Dropping -- 5.6.1 Uni ed Problem Description and Preliminaries -- 5.6.2 Asymptotic Properties of the Random Matrix Recursion -- 5.6.3 Approximation of the Stationary Distribution -- 5.7 Bibliographic Notes -- Appendix 5.A -- 5.A.1 Proof of Theorem 5.18 -- 5.A.2 Proof of Theorem 5.19 -- 5.A.3 Proof of Lemma 5.11 -- 5.A.4 Proof of Theorem 5.20 -- 5.A.5 Proof of Theorem 5.21.
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5.A.6 Proof of Theorem 5.22 -- References -- 6 Distributed State Estimation in an LSS -- 6.1 Introduction -- 6.2 Predictor Design With Local Measurements -- 6.2.1 Derivation of the Optimal Gain Matrix -- 6.2.2 Relations With the Kalman Filter -- 6.2.3 Robusti cation of the Distributed Predictor -- 6.3 Distributed State Filtering -- 6.4 Asymptotic Property of the Distributed Observers -- 6.5 Distributed State Estimation Through Neighbor Information Exchanges -- 6.6 Bibliographic Notes -- Appendix 6.A -- 6.A.1 Proof of Theorem 6.1 -- 6.A.2 Proof of Theorem 6.2 -- 6.A.3 Proof of Theorem 6.3 -- 6.A.4 Proof of Theorem 6.4 -- 6.A.5 Derivation of Eqs. (6.46) and (6.47) -- 6.A.6 Proof of Theorem 6.7 -- 6.A.7 Proof of Theorem 6.8 -- References -- 7 Stability and Robust Stability of a Large-Scale NCS -- 7.1 Introduction -- 7.2 A Networked System With Discrete-Time Subsystems -- 7.2.1 System Description -- 7.2.2 Stability of a Networked System -- 7.2.3 Robust Stability of a Networked System -- 7.3 A Networked System With Continuous-Time Subsystems -- 7.3.1 Modeling Errors Described by IQCs -- 7.3.2 Robust Stability With IQC-Described Modeling Errors -- 7.4 Concluding Remarks -- 7.5 Bibliographic Notes -- Appendix 7.A -- 7.A.1 Proof of Theorem 7.3 -- 7.A.2 Proof of Theorem 7.4 -- References -- 8 Control With Communication Constraints -- 8.1 Introduction -- 8.2 Entropies and Capacities of a Communication Channel -- 8.2.1 Entropy in Information Theory -- 8.2.2 Topological Entropy in Feedback Theory -- 8.2.3 Channel Capacities -- 8.3 Stabilization Over Communication Channel -- 8.3.1 Classical Approach for Quantized Control -- 8.4 Universal Lower Bound -- 8.5 Coder-Decoder Design -- 8.6 Extension to Lossy Channels -- 8.6.1 Erasure Channels -- 8.6.2 Gilbert-Elliott Channels -- 8.7 Bibliographic Notes -- References -- 9 Distributed Control for Large-Scale NCSs.
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9.1 Introduction -- 9.2 Consensus of Multiagent Systems -- 9.2.1 Communication Graph -- 9.2.2 Consensus of Multiagent Systems -- 9.3 Consensus Control With Relative State Feedback -- 9.3.1 Design of Consensus Gain -- 9.3.2 Extensions to Digraphs -- 9.3.3 Performance Analysis -- 9.3.4 Optimal Consensus Control for Second-Order Systems -- 9.4 Consensus Control With Relative Output Feedback -- 9.4.1 Distributed Observer-Based Protocol -- 9.4.2 Consensus Under Static Protocol -- 9.4.3 Consensus Under Dynamic Protocol -- 9.4.4 Multiagent Systems With Double Integrators -- 9.5 Formation Control for Multiagent Systems -- 9.5.1 Vehicle Formation With Double Integrators -- 9.5.2 Formation-Based Tracking Problem -- 9.6 Simulations and Experiments -- 9.6.1 Modeling -- 9.6.2 Simulation Results -- 9.7 Bibliographic Notes -- References -- 10 Structure Identi cation for Networked Systems -- 10.1 Introduction -- 10.2 Steady-State Data-Based Identi cation -- 10.2.1 Description of the Inference Procedure -- 10.2.2 Identi cation Algorithm -- Position Determination for Direct Regulations -- Estimation of Regulation Coef cients -- Determination of the Number of Direct Regulations -- 10.3 Absolute and Relative Variations in GRN Structure Estimations -- 10.3.1 Maximum Likelihood Estimation for Wild-Type Expression Level and Measurement Error Variance -- 10.3.2 Estimation of Relative Expression Level Variations -- 10.3.3 Estimation Algorithm -- 10.4 Estimation With Time Series Data -- 10.4.1 Robust Structure Identi cation Algorithm for GRNs -- 10.4.2 Convergence Analysis of the Robust Structure Identi cation Algorithm -- 10.5 Bibliographic Notes -- Appendix 10.A -- 10.A.1 Proof of Theorem 10.4 -- 10.A.2 Proof of Theorem 10.5 -- References -- 11 Attack Identi cation and Prevention in Networked Systems -- 11.1 Introduction -- 11.2 The SCADA System.
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11.3 Attack Prevention and System Transmission Zeros -- 11.3.1 Zero Dynamics and Transmission Zeros -- 11.3.2 Attack Prevention -- 11.4 Detection of Attacks -- 11.5 Identi cation of Attacks -- 11.6 System Security and Sensor/Actuator Placement -- 11.6.1 Some Properties of the Kalman Filter -- 11.6.2 Sensor Placements -- 11.6.3 Actuator Placements -- 11.7 Concluding Remarks -- 11.8 Bibliographic Notes -- Appendix 11.A -- 11.A.1 Proof of Theorem 11.7 -- References -- 12 Some Related Issues -- 12.1 Introduction -- 12.2 Cooperation Over Communications -- 12.2.1 Time Synchronization -- 12.2.2 State Consensus -- Fixed Topology Case -- Time-Varying Topology Case -- 12.3 Adaptive Mean-Field Games for Large Population Coupled ARX Systems With Unknown Coupling Strength -- Introduction -- Problem Formulation -- Control Design -- Closed-Loop Analysis -- 12.4 Other Topics and Theoretical Challenges -- 12.5 Bibliographic Notes -- Appendix 12.A -- 12.A.1 Proof of Theorem 12.5 -- References -- Index -- Back Cover.
Sprache:
Englisch
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