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E-raamat: Event-Based State Estimation: A Stochastic Perspective

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Event-Based State Estimation: A Stochastic PerspectiveSuurem pilt
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This book explores event-based estimation problems. It shows howseveral stochastic approaches are developed to maintain estimation performancewhen sensors perform their updates at slower rates only when needed.The self-contained presentation makes this book s uitablefor readers with no more than a basic knowledge of probability analysis,matrix algebra and linear systems. The introduction and literature review provideinformation, while the main content deals with estimation problems from fourdistinct angles in a stochastic setting, using numerous illustrative examplesand comparisons. The text elucidates both theoretical developments and theirapplications, and is rounded out by a review of open problems.This book is a valuable resource for researchers andstudents who wish to expand their knowledge and work in the area ofevent-triggered systems. At the same time, engineers andpractitioners in industrial process control will benefit from theevent-triggering technique t

hat reduces communication costs and improves energyefficiency in wireless automation applications .

Introduction.- Linear Gaussian Systems and Event-Based State Estimation.- Event-Triggered Sampling.- Approximate Optimal Filtering Approaches.- Constrained Optimization Approach.- Set-Valued Filtering Approach.- Probabilistic Approach.- Communications Rate Analysis.- Open Problems.- Appendices: Brief Review of Probability Theory; Linear Estimation Theory.

Arvustused

This book addresses essentially the engineers (or researchers and students interested in the area of event-trigged systems). The subject turns around event-based estimation problems in a stochastic setting. This document is self-contained and it is readable with just a basic knowledge of probability theory, Kalman filtering theory, and linear algebra. This book is clear and well written. The results presented are proven, and each chapter contains notes and references. (Bénédicte Puig, zbMATH 1331.62010, 2016)

1 Introduction
1(22)
1.1 Sampled-Data Systems and Event-Based Sampling
1(4)
1.2 A Brief History of Event-Based Sampled-Data Systems
5(2)
1.3 Why Event-Based Estimation?
7(2)
1.4 Literature Review of Event-Based Estimation
9(5)
1.4.1 Design of Event-Triggering Strategies
9(1)
1.4.2 Event-Based Estimator Design---Stochastic Formulations
10(2)
1.4.3 Event-Based Estimator Design---Deterministic Formulations
12(1)
1.4.4 Some Applications
13(1)
1.5 Scope and Organization of the Book
14(9)
References
16(7)
2 Event-Triggered Sampling
23(10)
2.1 Periodic and Event-Based Sampling
23(3)
2.1.1 Periodic Sampling
24(1)
2.1.2 Event-Based Sampling
25(1)
2.1.3 Comparison
26(1)
2.2 Optimal Stopping Approach to Event-Triggered Sampling
26(4)
2.2.1 Choice of Terminal Control
27(1)
2.2.2 Optimal Deterministic Switching
28(1)
2.2.3 Optimal Event-Based Switching
29(1)
2.3 Summary
30(1)
2.4 Notes and References
31(2)
References
31(2)
3 Linear Gaussian Systems and Event-Based State Estimation
33(14)
3.1 Linear Gaussian Systems
33(2)
3.2 Event-Triggering Schemes
35(6)
3.2.1 Deterministic Event-Triggering Conditions
36(3)
3.2.2 Stochastic Event-Triggering Conditions
39(2)
3.2.3 Relationship Between the Stochastic and Deterministic Event-Triggering Conditions
41(1)
3.3 Basic Problems in Event-Based State Estimation
41(2)
3.3.1 Estimator Design
41(1)
3.3.2 Performance Assessment
42(1)
3.3.3 Event-Triggering Condition Design
42(1)
3.4 A Note on Commonly Used Notation
43(1)
3.5 Kalman Filter with Intermittent Observations
43(2)
3.6 Notes and References
45(2)
References
45(2)
4 Approximate Event-Triggering Approaches
47(30)
4.1 The State Estimation Problem and the Exact Solution
47(3)
4.2 Approximate Gaussian Approach
50(12)
4.2.1 Basic Assumption and Problem Statement
50(2)
4.2.2 Approximate Event-Based Estimator Design
52(8)
4.2.3 Experimental Verification
60(2)
4.3 Approximate Gaussian Approach: A Special Case
62(4)
4.3.1 System Description and Estimator Design
63(3)
4.3.2 Communication Rate Analysis
66(1)
4.4 Sum of Gaussians Approach
66(7)
4.4.1 Estimation Procedure
67(3)
4.4.2 Asymptotic Properties of the Estimation Error Covariance
70(1)
4.4.3 An Illustrative Example and Comparison
71(2)
4.5 Discussions
73(1)
4.6 Notes and References
74(3)
References
75(2)
5 A Constrained Optimization Approach
77(32)
5.1 Problem Formulation
77(3)
5.2 Solution to the Optimal Estimation Problem
80(4)
5.3 One-Step State Estimation
84(3)
5.4 A Framework for Communication Rate Analysis
87(11)
5.5 Illustrative Examples
98(7)
5.5.1 Example 1
99(3)
5.5.2 Example 2: Sensorless Event-Based Estimation of a DC Motor System
102(3)
5.6 Summary
105(1)
5.7 Notes and References
106(3)
References
107(2)
6 A Stochastic Event-Triggering Approach
109(34)
6.1 Problem Formulation
109(4)
6.2 Optimal Estimator Design
113(7)
6.2.1 Open-Loop Schedule
114(5)
6.2.2 Closed-Loop Schedule
119(1)
6.3 Performance Analysis
120(14)
6.3.1 Open-Loop Schedule
122(5)
6.3.2 Closed-Loop Schedule
127(2)
6.3.3 Design of Event Parameters
129(5)
6.4 Numerical Examples
134(5)
6.4.1 Performance of MMSE Estimates for the Open-Loop and Closed-Loop Schedules
134(2)
6.4.2 Design of Event Parameters
136(1)
6.4.3 Comparison Between MMSE Estimates for the Closed-Loop Schedule and the Approximate MMSE Estimates
137(2)
6.5 Summary
139(1)
6.6 Notes and References
139(4)
References
140(3)
7 A Set-Valued Filtering Approach
143(40)
7.1 Set-Valued Filtering and Event-Based Estimation
143(2)
7.2 Problem Setup
145(6)
7.2.1 Event-Based State Estimation
145(2)
7.2.2 Set-Valued Filters
147(3)
7.2.3 Problems Considered
150(1)
7.3 Sensor Fusion
151(6)
7.4 Asymptotic Properties of the Set of Estimation Means
157(7)
7.5 Performance Improvement
164(5)
7.6 Event-Triggering Condition Design
169(3)
7.7 Examples
172(7)
7.7.1 Example 1
172(3)
7.7.2 Example 2: Set-Valued Event-Based Estimation for the Drive Train System of a Wind Turbine
175(4)
7.8 Summary
179(1)
7.9 Notes and References
180(3)
References
181(2)
8 Summary and Open Problems
183(6)
8.1 Summary
183(1)
8.2 A Few Open Problems
184(5)
8.2.1 Optimal Event-Based Sampling
184(1)
8.2.2 Event-Based State Estimation with Packet Dropouts and Time Delays
185(1)
8.2.3 State Estimation with Partially Unknown Event-Triggering Schemes
185(1)
8.2.4 Complete Communication Rate Analysis
186(1)
8.2.5 Event-Based Joint Parameter and State Estimation
186(1)
8.2.6 Fundamental Limitation of Event-Based Estimation
186(1)
References
187(2)
Appendix A Review of Probability and Random Processes 189(6)
Appendix B Optimal Estimation 195(12)
Index 207
Dawei Shi was born in Shandong Province, China. He received his B.Eng. degree in Electrical Engineering and its Automation from the Beijing Institute of Technology in 2008. In 2010, he received the financial support from the China Scholarship Council and the Provost Scholarship from the University of Alberta, where he received the Ph.D. degree on control systems in November 2014. From December 2014, he has been appointed as an Associate Professor at the School of Automation, Beijing Institute of Technology, China. In 2009, he received the Best Student Paper Award at the IEEE International Conference on Automation and Logistics.  His research interests include event-based control and estimation, robust model predictive control and tuning, and wireless sensor networks. He has authored or co-authored 18 papers, most of which have been published in the top journals and conferences in the field of control systems. He also applied for a US patent as the first inventor in March2014, supported by Honeywell Process Solutions, Vancouver. He is a reviewer for a number of international journals and conferences, including Automatica, IEEE Transactions on Automatic Control, Systems & Control Letters, IEEE Conference on Decision and Control, American Control Conference, and IFAC World Congress.

   Ling Shi received his B.S. degree in Electrical and Electronic Engineering from the Hong Kong University of Science and Technology in 2002 and Ph.D. degree in Control and Dynamical Systems from California Institute of Technology in 2008. He  is currently an Associate Professor in the  Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology. His research  interests include modeling and control of cyber-physical systems, and sensor data scheduling and sensor fusion. He has published over 30 journals papers and 60 conference papers. He is a member of the IFAC Technical Committee on Networked Systems since Oct 2011, and served in many conference editorial boards including the 2013, 2014, and 2015 European Control Conference. He currently serves as the secretary and treasurer of the Hong Kong Automatic Control Association for the term 2014-2017. 







Tongwen Chen is presently a Professor of Electrical and Computer Engineering at the University of Alberta, Edmonton, Canada. He received the B.Eng. degree in Automation and Instrumentation from Tsinghua University (Beijing) in 1984, and the M.A.Sc. and Ph.D. degrees in Electrical Engineering from the University of Toronto in 1988 and 1991, respectively. He has worked on several research areas related to computer control systems, including sampled-data control, multirate systems, networked control systems, and more recently, event-triggered control, and industrial alarm monitoring.  In these areas, he has co-authored over 100 journal publications, a graduate textbook, "Optimal Sampled-Data Control Systems"(Springer, 1995), and a research monograph, "Capturing Connectivity and Causality in Complex Industrial Processes" (SpringerBrief, 2014). Moreover, some of his research results have been applied and implemented in the Canadian industry, yielding better process operation and safety. He has been an Associate Editor for several international journals, including IEEE Transactions on Automatic Control, Automatica, and Systems and Control Letters. He is a Fellow of IEEE, IFAC, as well as Canadian Academy of Engineering. 





 
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