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Estimation and Control of Large-Scale Networked Systems [Pehme köide]

(Professor, Department of Automation, Tsinghua University, Beijing, China), (School of of Materials Science and Engineering, Southeast University, Nanjing, P.R. China), (Associate Professor, Tsinghua University, Beijing, China)
  • Formaat: Paperback / softback, 496 pages, kõrgus x laius: 235x191 mm, kaal: 1040 g
  • Ilmumisaeg: 27-Jun-2018
  • Kirjastus: Butterworth-Heinemann Inc
  • ISBN-10: 0128053119
  • ISBN-13: 9780128053119
Teised raamatud teemal:
Estimation and Control of Large-Scale Networked SystemsSuurem pilt
  • Formaat: Paperback / softback, 496 pages, kõrgus x laius: 235x191 mm, kaal: 1040 g
  • Ilmumisaeg: 27-Jun-2018
  • Kirjastus: Butterworth-Heinemann Inc
  • ISBN-10: 0128053119
  • ISBN-13: 9780128053119
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780128053119.html
Märksõnad:

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
Preface xv
Acknowledgments xvii
Notation and Symbols xix
Chapter 1 Introduction
1(12)
1.1 A General View on Control System Design
1(2)
1.2 Communication and Control
3(3)
1.3 Book Contents
6(5)
1.3.1 Controllability and Observability of a Control System
7(1)
1.3.2 Centralized and Distributed State Estimations
8(1)
1.3.3 State Estimations and Control With Imperfect Communications
8(1)
1.3.4 Verification of Stability and Robust Stability
9(1)
1.3.5 Distributed Controller Design for an LSS
9(1)
1.3.6 Structure Identification for an LSS
10(1)
1.3.7 Attack Estimation/Identification and Other Issues
10(1)
1.4 Bibliographic Notes
11(2)
References
11(2)
Chapter 2 Background Mathematical Results
13(28)
2.1 Linear Space and Linear Algebra
13(11)
2.1.1 Vector and Matrix Norms
20(2)
2.1.2 Hamiltonian Matrices and Distance Among Positive Definite Matrices
22(2)
2.2 Generalized Inverse of a Matrix
24(2)
2.3 Some Useful Transformations
26(3)
2.4 Set Function and Submodularity
29(3)
2.5 Probability and Random Process
32(4)
2.6 Markov Process and Semi-Markov Process
36(3)
2.7 Bibliographic Notes
39(2)
References
39(2)
Chapter 3 Controllability and Observability of an LSS
41(44)
3.1 Introduction
41(1)
3.2 Controllability and Observability of an LTI System
42(11)
3.2.1 Minimal Number of Inputs/Outputs Guaranteeing Controllability/Observability
47(3)
3.2.2 A Parameterization of Desirable Input/Output Matrices
50(2)
3.2.3 Some Nitpicking
52(1)
3.3 A General Model for an LSS
53(3)
3.4 Controllability and Observability for an LSS
56(15)
3.4.1 Subsystem Transmission Zeros and Observability of an LSS
59(3)
3.4.2 Observability Verification
62(2)
3.4.3 A Condition for Controllability and Its Verification
64(1)
3.4.4 In/Out-degree and Controllability/Observability of a Networked System
65(6)
3.5 Construction of Controllable/Observable Networked Systems
71(2)
3.6 Bibliographic Notes
73(12)
Appendix 3.A
74(1)
3.A.1 Proof of Theorem 3.4
74(2)
3.A.2 Proof of Theorem 3.8
76(3)
3.A.3 Proof of Theorem 3.9
79(2)
3.A.4 Proof of Theorem 3.10
81(2)
References
83(2)
Chapter 4 Kalman Filtering and Robust Estimation
85(40)
4.1 Introduction
85(1)
4.2 State Estimation and Observer Design
85(3)
4.3 Kalman Filter as a Maximum Likelihood Estimator
88(10)
4.3.1 Derivation of the Kalman Filter
90(6)
4.3.2 Convergence Property of the Kalman Filter
96(2)
4.4 Recursive Robust State Estimation Through Sensitivity Penalization
98(17)
4.4.1 Estimation Algorithm
98(4)
4.4.2 Derivation of the Robust Estimator
102(5)
4.4.3 Asymptotic Properties of the Robust State Estimator
107(6)
4.4.4 Boundedness of Estimation Errors
113(2)
4.5 Bibliographic Notes
115(10)
Appendix 4.A
116(1)
4.A.1 Proof of Theorem 4.1
116(2)
4.A.2 Proof of Theorem 4.3
118(5)
References
123(2)
Chapter 5 State Estimation With Random Data Droppings
125(60)
5.1 Introduction
125(1)
5.2 Intermittent Kalman Filtering (IKF)
126(6)
5.2.1 The IKF Algorithm
127(2)
5.2.2 Mean Square Stability of the IKF
129(2)
5.2.3 Weak Convergence of the IKF
131(1)
5.3 IKF With Switching Sensors
132(12)
5.3.1 Mean Square Stability
135(6)
5.3.2 Second-Order Systems
141(2)
5.3.3 Extension to Higher-Order Systems
143(1)
5.4 IKF With Coded Measurement Transmission
144(7)
5.4.1 Linear Temporal Coding
144(1)
5.4.2 The MMSE Filter
145(2)
5.4.3 Mean Square Stability
147(4)
5.5 Robust State Estimation With Random Data Droppings
151(5)
5.5.1 System With Parametric Errors
151(1)
5.5.2 Robust State Estimator
152(2)
5.5.3 Convergence of the Robust State Estimator
154(2)
5.6 Asymptotic Properties of State Estimations With Random Data Dropping
156(12)
5.6.1 Unified Problem Description and Preliminaries
157(1)
5.6.2 Asymptotic Properties of the Random Matrix Recursion
158(5)
5.6.3 Approximation of the Stationary Distribution
163(5)
5.7 Bibliographic Notes
168(17)
Appendix 5.A
168(1)
5.A.1 Proof of Theorem 5.18
168(4)
5.A.2 Proof of Theorem 5.19
172(4)
5.A.3 Proof of Lemma 5.11
176(1)
5.A.4 Proof of Theorem 5.20
176(2)
5.A.5 Proof of Theorem 5.21
178(3)
5.A.6 Proof of Theorem 5.22
181(1)
References
182(3)
Chapter 6 Distributed State Estimation in an LSS
185(52)
6.1 Introduction
185(1)
6.2 Predictor Design With Local Measurements
186(18)
6.2.1 Derivation of the Optimal Gain Matrix
187(9)
6.2.2 Relations With the Kalman Filter
196(4)
6.2.3 Robustification of the Distributed Predictor
200(4)
6.3 Distributed State Filtering
204(7)
6.4 Asymptotic Property of the Distributed Observers
211(2)
6.5 Distributed State Estimation Through Neighbor Information Exchanges
213(6)
6.6 Bibliographic Notes
219(18)
Appendix 6.A
219(1)
6.A.1 Proof of Theorem 6.1
219(3)
6.A.2 Proof of Theorem 6.2
222(2)
6.A.3 Proof of Theorem 6.3
224(2)
6.A.4 Proof of Theorem 6.4
226(2)
6.A.5 Derivation of Eqs. (6.46) and (6.47)
228(2)
6.A.6 Proof of Theorem 6.7
230(2)
6.A.7 Proof of Theorem 6.8
232(2)
References
234(3)
Chapter 7 Stability and Robust Stability of a Large-Scale NCS
237(28)
7.1 Introduction
237(1)
7.2 A Networked System With Discrete-Time Subsystems
238(11)
7.2.1 System Description
238(1)
7.2.2 Stability of a Networked System
239(9)
7.2.3 Robust Stability of a Networked System
248(1)
7.3 A Networked System With Continuous-Time Subsystems
249(8)
7.3.1 Modeling Errors Described by IQCs
250(1)
7.3.2 Robust Stability With IQC-Described Modeling Errors
251(6)
7.4 Concluding Remarks
257(1)
7.5 Bibliographic Notes
257(8)
Appendix 7.A
258(1)
7.A.1 Proof of Theorem 7.3
258(2)
7.A.2 Proof of Theorem 7.4
260(3)
References
263(2)
Chapter 8 Control With Communication Constraints
265(18)
8.1 Introduction
265(1)
8.2 Entropies and Capacities of a Communication Channel
266(4)
8.2.1 Entropy in Information Theory
266(1)
8.2.2 Topological Entropy in Feedback Theory
267(1)
8.2.3 Channel Capacities
268(2)
8.3 Stabilization Over Communication Channel
270(2)
8.3.1 Classical Approach for Quantized Control
270(2)
8.4 Universal Lower Bound
272(1)
8.5 Coder-Decoder Design
273(5)
8.6 Extension to Lossy Channels
278(2)
8.6.1 Erasure Channels
278(1)
8.6.2 Gilbert-Elliott Channels
279(1)
8.7 Bibliographic Notes
280(3)
References
281(2)
Chapter 9 Distributed Control for Large-Scale NCSs
283(38)
9.1 Introduction
283(1)
9.2 Consensus of Multiagent Systems
284(1)
9.2.1 Communication Graph
284(1)
9.2.2 Consensus of Multiagent Systems
285(1)
9.3 Consensus Control With Relative State Feedback
285(15)
9.3.1 Design of Consensus Gain
286(5)
9.3.2 Extensions to Digraphs
291(3)
9.3.3 Performance Analysis
294(2)
9.3.4 Optimal Consensus Control for Second-Order Systems
296(4)
9.4 Consensus Control With Relative Output Feedback
300(8)
9.4.1 Distributed Observer-Based Protocol
300(1)
9.4.2 Consensus Under Static Protocol
301(2)
9.4.3 Consensus Under Dynamic Protocol
303(2)
9.4.4 Multiagent Systems With Double Integrators
305(3)
9.5 Formation Control for Multiagent Systems
308(5)
9.5.1 Vehicle Formation With Double Integrators
309(1)
9.5.2 Formation-Based Tracking Problem
310(3)
9.6 Simulations and Experiments
313(4)
9.6.1 Modeling
313(2)
9.6.2 Simulation Results
315(2)
9.7 Bibliographic Notes
317(4)
References
319(2)
Chapter 10 Structure Identification for Networked Systems
321(46)
10.1 Introduction
321(2)
10.2 Steady-State Data-Based Identification
323(12)
10.2.1 Description of the Inference Procedure
324(3)
10.2.2 Identification Algorithm
327(8)
10.3 Absolute and Relative Variations in GRN Structure Estimations
335(8)
10.3.1 Maximum Likelihood Estimation for Wild-Type Expression Level and Measurement Error Variance
337(3)
10.3.2 Estimation of Relative Expression Level Variations
340(2)
10.3.3 Estimation Algorithm
342(1)
10.4 Estimation With Time Series Data
343(13)
10.4.1 Robust Structure Identification Algorithm for GRNs
345(6)
10.4.2 Convergence Analysis of the Robust Structure Identification Algorithm
351(5)
10.5 Bibliographic Notes
356(11)
Appendix 10.A
356(1)
10.A.1 Proof of Theorem 10.4
356(3)
10.A.2 Proof of Theorem 10.5
359(3)
References
362(5)
Chapter 11 Attack Identification and Prevention in Networked Systems
367(60)
11.1 Introduction
367(3)
11.2 The SCADA System
370(3)
11.3 Attack Prevention and System Transmission Zeros
373(17)
11.3.1 Zero Dynamics and Transmission Zeros
377(9)
11.3.2 Attack Prevention
386(4)
11.4 Detection of Attacks
390(2)
11.5 Identification of Attacks
392(7)
11.6 System Security and Sensor/Actuator Placement
399(16)
11.6.1 Some Properties of the Kalman Filter
401(4)
11.6.2 Sensor Placements
405(5)
11.6.3 Actuator Placements
410(5)
11.7 Concluding Remarks
415(1)
11.8 Bibliographic Notes
415(12)
Appendix 11.A
415(1)
11.A.1 Proof of Theorem 11.7
415(3)
References
418(9)
Chapter 12 Some Related Issues
427(46)
12.1 Introduction
421(1)
12.2 Cooperation Over Communications
422(23)
12.2.1 Time Synchronization
422(2)
12.2.2 State Consensus
424(21)
12.3 Adaptive Mean-Field Games for Large Population Coupled ARX Systems With Unknown Coupling Strength
445(15)
Introduction
445(15)
12.4 Other Topics and Theoretical Challenges
460(3)
12.5 Bibliographic Notes
463(10)
Appendix 12.A
464(1)
12.A.1 Proof of Theorem 12.5
464(4)
References
468(5)
Index 473
Tong Zhou was born in Hunan Province, China, in 1964. He received the B.S. and M.S. degrees from the University of Electronic Science and Technology of China, Chengdu, China, in 1984 and 1989, respectively, another M.S.degree from Kanazawa University, Ishikawa Prefecture, Japan, in 1991, and the Ph.D. degree from Osaka University, Osaka, Japan, in 1994. After visiting several universities in The Netherlands, China, and Japan, he joined Tsinghua University, Beijing, China, in 1999, where he is currently a Professor of control theory and control engineering. His current research interests include robust estimation and control, system identification, signal processing, hybrid systems, and their applications to real-world problems in molecular cell biology, spatio-temporal systems, magnetic levitation systems, and communication systems. Dr. Zhou was a recipient of the First-Class Natural Science Prize in 2003 from the Ministry of Education, China, and a recipient of the National Outstanding Youth Foundation of China in 2006. He has served as an Associate Editor of the IEEE TRANSACTIONS ON AUTOMATIC CONTROL continuously for two terms (6 years), and is now on the editorial board of AUTOMATICA (continuously in the 4th term). He is a Fellow of the IEEE. Keyou You received the B.S. degree in Statistical Science from Sun Yat-sen University, Guangzhou, China, in 2007 and the Ph.D. degree in Electrical and Electronic Engineering from Nanyang Technological University (NTU), Singapore, in 2012. After briefly working as a Research Fellow at NTU, he joined Tsinghua University in Beijing, China where he is now an Associate Professor in the Department of Automation. He held visiting positions at Politecnico di Torino, The Hong Kong University of Science and Technology, The University of Melbourne and etc. His current research interests include networked control systems, distributed algorithms, and their applications. Dr. You received the Guan Zhaozhi award at the 29th Chinese Control Conference in 2010, and the CSC-IBM China Faculty Award in 2014. He was selected to the National Young 1000-plan Talent Program in 2014, and received the Outstanding Young Scholar Fund from the National Natural Science Foundation of China in 2017. Li Tao is a Professor in the School of Materials Science and Engineering at Southeast University, Nanjing, China. His research interests focus on 2D materials and flexible devices, microsystem and nanoengineering platforms. He is a co-inventor of mono- and multi-layer silicene transistors, 12 wafer-scale graphene on evaporated Cu and GHz flexible graphene electronics.