Estimation and control of large-scale networked systems

Estimation and control of large-scale networked systems

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. Re...

Descripción completa

Detalles Bibliográficos
Otros Autores: Zhou, Tong, author (author), You, Keyou, author, Li, Tao, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Oxford : Butterworth-Heinemann 2018.
Edición:First edition
Materias:
Automatic control.
Large scale systems.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630707906719
Tabla de Contenidos:
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

Ejemplares similares