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Analysis and Design of Networked Control Systems 2015 ed. [Hardback]

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  • Formāts: Hardback, 321 pages, height x width: 235x155 mm, weight: 6878 g, 21 Illustrations, color; 34 Illustrations, black and white; XII, 321 p. 55 illus., 21 illus. in color., 1 Hardback
  • Sērija : Communications and Control Engineering
  • Izdošanas datums: 19-Jan-2015
  • Izdevniecība: Springer London Ltd
  • ISBN-10: 1447166140
  • ISBN-13: 9781447166146
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Analysis and Design of Networked Control Systems 2015 ed.Palielināt
  • Formāts: Hardback, 321 pages, height x width: 235x155 mm, weight: 6878 g, 21 Illustrations, color; 34 Illustrations, black and white; XII, 321 p. 55 illus., 21 illus. in color., 1 Hardback
  • Sērija : Communications and Control Engineering
  • Izdošanas datums: 19-Jan-2015
  • Izdevniecība: Springer London Ltd
  • ISBN-10: 1447166140
  • ISBN-13: 9781447166146
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781447166146.html
Keywords:
This monograph focuses on characterizing the stability and performance consequences of inserting limited-capacity communication networks within a control loop. The text shows how integration of the ideas of control and estimation with those of communication and information theory can be used to provide important insights concerning several fundamental problems such as:

· minimum data rate for stabilization of linear systems over noisy channels;

· minimum network requirement for stabilization of linear systems over fading channels; and

· stability of Kalman filtering with intermittent observations.

A fundamental link is revealed between the topological entropy of linear dynamical systems and the capacities of communication channels. The design of a logarithmic quantizer for the stabilization of linear systems under various network environments is also extensively discussed and solutions to many problems of Kalman filtering with intermittent observations are demonstrated.

Analysis and Design of Networked Control Systems will interest control theorists and engineers working with networked systems and may also be used as a resource for graduate students with backgrounds in applied mathematics, communications or control who are studying such systems.
1 Overview of Networked Control Systems
1(8)
1.1 Introduction and Motivation
1(4)
1.1.1 Components of NCS
2(1)
1.1.2 Brief History of NCS
3(1)
1.1.3 Challenges in NCS
4(1)
1.2 Preview of the Book
5(4)
References
7(2)
2 Entropies and Capacities in Networked Control Systems
9(20)
2.1 Entropies
9(2)
2.1.1 Entropy in Information Theory
9(1)
2.1.2 Topological Entropy in Feedback Theory
10(1)
2.2 Channel Capacities
11(3)
2.2.1 Noiseless Channels
12(1)
2.2.2 Noisy Channels
12(2)
2.3 Control Over Communication Networks
14(4)
2.3.1 Quantized Control Over Noiseless Networks
14(2)
2.3.2 Quantized Control Over Noisy Networks
16(2)
2.4 Estimation Over Communication Networks
18(5)
2.4.1 Quantized Estimation Over Noiseless Networks
18(2)
2.4.2 Data-Driven Communication for Estimation
20(1)
2.4.3 Estimation Over Noisy Networks
21(2)
2.5 Open Problems
23(6)
References
24(5)
3 Data Rate Theorem for Stabilization Over Noiseless Channels
29(10)
3.1 Problem Statement
29(2)
3.2 Classical Approach for Quantized Control
31(1)
3.3 Data Rate Theorem for Stabilization
31(5)
3.3.1 Proof of Necessity
32(1)
3.3.2 Proof of Sufficiency
33(3)
3.4 Summary
36(3)
References
36(3)
4 Data Rate Theorem for Stabilization Over Erasure Channels
39(14)
4.1 Problem Formulation
39(2)
4.2 Single Input Case
41(7)
4.2.1 Proof of Necessity
42(2)
4.2.2 Proof of Sufficiency
44(4)
4.3 Multiple Input Case
48(3)
4.4 Summary
51(2)
References
51(2)
5 Data Rate Theorem for Stabilization Over Gilbert-Elliott Channels
53(30)
5.1 Problem Formulation
53(2)
5.2 Preliminaries
55(1)
5.2.1 Random Down Sampling
55(1)
5.2.2 Statistical Properties of Sojourn Times
55(1)
5.3 Scalar Systems
56(7)
5.3.1 Noise Free Systems with Bounded Initial Support
56(2)
5.3.2 Proof of Necessity
58(2)
5.3.3 Proof of Sufficiency
60(3)
5.4 General Stochastic Scalar Systems
63(10)
5.4.1 Proof of Necessity
64(3)
5.4.2 Proof of Sufficiency
67(6)
5.5 Vector Systems
73(7)
5.5.1 Real Jordan Form
73(1)
5.5.2 Necessity
73(2)
5.5.3 Sufficiency
75(5)
5.5.4 An Example
80(1)
5.6 Summary
80(3)
References
81(2)
6 Stabilization of Linear Systems Over Fading Channels
83(40)
6.1 Problem Formulation
83(4)
6.2 State Feedback Case
87(8)
6.2.1 Parallel Transmission Strategy
93(1)
6.2.2 Serial Transmission Strategy
94(1)
6.3 Output Feedback Case
95(6)
6.3.1 SISO Plants
97(1)
6.3.2 Triangularly Decoupled Plants
98(3)
6.4 Extension and Application
101(5)
6.4.1 Stabilization Over Output Fading Channels
101(2)
6.4.2 Stabilization of a Finite Platoon
103(3)
6.5 Channel Processing and Channel Feedback
106(2)
6.6 Power Constraint
108(12)
6.6.1 Feedback Stabilization
110(5)
6.6.2 Performance Design
115(3)
6.6.3 Numerical Example
118(2)
6.7 Summary
120(3)
References
120(3)
7 Stabilization of Linear Systems via Infinite-Level Logarithmic Quantization
123(26)
7.1 State Feedback Case
124(9)
7.1.1 Logarithmic Quantization
124(2)
7.1.2 Sector Bound Approach
126(7)
7.2 Output Feedback Case
133(3)
7.2.1 Quantized Control
133(1)
7.2.2 Quantized Measurements
134(2)
7.3 Stabilization of MIMO Systems
136(5)
7.3.1 Quantized Control
136(4)
7.3.2 Quantized Measurements
140(1)
7.4 Quantized Quadratic Performance Control
141(3)
7.5 Quantized H∞ Control
144(3)
7.6 Summary
147(2)
References
148(1)
8 Stabilization of Linear Systems via Finite-Level Logarithmic Quantization
149(26)
8.1 Quadratic Stabilization via Finite-level Quantization
149(11)
8.1.1 Finite-level Quantizer
149(4)
8.1.2 Number of Quantization Levels
153(3)
8.1.3 Robustness Against Additive Noises
156(2)
8.1.4 Illustrative Examples
158(2)
8.2 Attainability of the Minimum Data Rate for Stabilization
160(14)
8.2.1 Problem Simplification
161(2)
8.2.2 Network Configuration
163(2)
8.2.3 Quantized Control Feedback
165(6)
8.2.4 Quantized State Feedback
171(3)
8.3 Summary
174(1)
References
174(1)
9 Stabilization of Markov Jump Linear Systems via Logarithmic Quantization
175(18)
9.1 State Feedback Case
175(13)
9.1.1 Feedback Stabilization
178(6)
9.1.2 Special Schemes
184(1)
9.1.3 Mode Estimation
185(3)
9.2 Stabilization Over Lossy Channels
188(3)
9.2.1 Binary Dropouts Model
188(2)
9.2.2 Bounded Dropouts Model
190(1)
9.2.3 Extension to Output Feedback
191(1)
9.3 Summary
191(2)
References
192(1)
10 Kalman Filtering with Quantized Innovations
193(12)
10.1 Problem Formulation
193(2)
10.2 Quantized Innovations Kalman Filter
195(6)
10.2.1 Multi-level Quantized Filtering
195(3)
10.2.2 Optimal Quantization Thresholds
198(1)
10.2.3 Convergence Analysis
199(2)
10.3 Robust Quantization
201(1)
10.4 A Numerical Example
202(2)
10.5 Summary
204(1)
References
204(1)
11 LQG Control with Quantized Innovation Kalman Filter
205(18)
11.1 Problem Formulation
205(2)
11.2 Separation Principle
207(6)
11.3 State Estimator Design
213(4)
11.4 Controller Design
217(1)
11.5 An Illustrative Example
218(2)
11.6 Summary
220(3)
References
221(2)
12 Kalman Filtering with Faded Measurements
223(16)
12.1 Problem Formulation
223(2)
12.2 Stability Analysis of Kalman Filter with Fading
225(9)
12.2.1 Preliminaries
225(7)
12.2.2 Mean Covariance Stability
232(2)
12.3 A Numerical Example
234(2)
12.4 Summary
236(3)
References
236(3)
13 Kalman Filtering with Packet Losses
239(30)
13.1 Networked Estimation
239(3)
13.1.1 Intermittent Kalman Filter
241(1)
13.1.2 Stability Notions
242(1)
13.2 Equivalence of the Two Stability Notions
242(4)
13.3 Second-Order Systems
246(1)
13.4 Higher-Order Systems
247(2)
13.4.1 Non-degenerate Systems
248(1)
13.5 Illustrative Examples
249(2)
13.6 Proofs
251(16)
13.6.1 Proof of Theorem 13.3
253(3)
13.6.2 Proof of Theorem 13.4
256(3)
13.6.3 Proofs of Results in Sect. 13.4
259(8)
13.7 Summary
267(2)
References
267(2)
14 Kalman Filtering with Scheduled Measurements
269(24)
14.1 Networked Estimation
269(2)
14.1.1 Scheduling Problems
270(1)
14.2 Controllable Scheduler
271(10)
14.2.1 An Approximate MMSE Estimator
271(3)
14.2.2 An Illustrative Example
274(3)
14.2.3 Stability Analysis
277(4)
14.3 Uncontrollable Scheduler
281(9)
14.3.1 Intermittent Kalman Filter
281(2)
14.3.2 Second-Order System
283(5)
14.3.3 Higher-Order System
288(2)
14.4 Summary
290(3)
References
291(2)
15 Parameter Estimation with Scheduled Measurements
293(24)
15.1 Innovation Based Scheduler
293(2)
15.2 Maximum Likelihood Estimation
295(7)
15.2.1 ML Estimator
295(3)
15.2.2 Estimation Performance
298(1)
15.2.3 Optimal Scheduler
299(3)
15.3 Naive Estimation
302(1)
15.4 Iterative ML Estimation
303(4)
15.4.1 Adaptive Scheduler
304(3)
15.5 Proof of Theorem 15.1
307(3)
15.6 EM-Based Estimation
310(3)
15.6.1 Design of k
313(1)
15.7 Numerical Example
313(2)
15.8 Summary
315(2)
References
316(1)
Appendix A On Matrices 317(2)
Index 319
Keyou You was born in Jiangxi Province, China, in 1985. He 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, Singapore, in 2012. He was with the ARC Center for Complex Dynamic Systems and Control, the University of Newcastle, Australia, as a visiting scholar from May 2010 to July 2010, and with the Sensor Network Laboratory at Nanyang Technological University as a Research Fellow from June 2011 to June 2012. Since July 2012, he has been with the Department of Automation, Tsinghua University, China as an Assistant Professor. From May 2013 to July 2013, he held a visiting position in The Hong Kong University of Science and Technology, Hong Kong. His current research interests include control and estimation of networked systems, distributed control and estimation, and sensor network. Dr. You won the Guan Zhaozhi best paper award at the 29th Chinese Control Conference, Beijing, China, in 2010, and was selected to the National 1000 Young Talents Plan in 2013. 

Nan Xiao received the B.E. and M.E. degrees in electrical engineering and automation from Tianjin University, China in 2005 and 2007, respectively, and the Ph.D. degree in electrical and electronic engineering from Nanyang Technological University, Singapore in 2012. Currently, he is a postdoctoral fellow in the school of electrical and electronic engineering, Nanyang Technological University. His research interests include networked control systems, and robust and stochastic control theory.

Lihua Xie received the Ph.D. degree in electrical engineering from the University of Newcastle, Australia. Currently, he is a professor and head of division of control and instrumentation in the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. His research interests include robust control andestimation, networked control systems, sensor networks, time delay systems and control of hard disk drive systems. Dr. Xie is an editor of the IET Book Series on Control and has served as an Associate Editor of several journals including the IEEE Transactions on Automatic Control, Automatica etc. He is a Fellow of the IEEE and Fellow of the International Federation of Automatic Control.