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E-raamat: Variance-Constrained Multi-Objective Stochastic Control and Filtering

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Variance-Constrained Multi-Objective Stochastic Control and FilteringSuurem pilt
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Variance-Constrained Multi-Objective Stochastic Control and Filtering establishes a unified framework for filtering and control problems for various discrete-time nonlinear stochastic systems with engineering-oriented complications such as parameter uncertainties, missing measurements, sensor/actuator faults and degraded outputs, which are typical phenomena resulting from the complexity in nowadays complex systems. Multiple performance requirements are simultaneously considered, which include the regional stability, steady-state variance, robustness, disturbance rejection attenuation, integrity against missing measurements, reliability against sensor/actuator failures, dissipativity and energy constraint. A set of latest techniques are covered to handle the emerging mathematical/computational challenges involved. This book covers this developing area of control and filtering theories for stochastic systems with multiple objective and variance constraints typically resulting from complex environments.
Preface ix
Series Preface xi
Acknowledgements xiii
List of Abbreviations
xv
List of Figures
xvii
1 Introduction
1(18)
1.1 Analysis and Synthesis of Nonlinear Stochastic Systems
3(2)
1.1.1 Nonlinear Systems
3(2)
1.1.2 Stochastic Systems
5(1)
1.2 Multi-Objective Control and Filtering with Variance Constraints
5(8)
1.2.1 Covariance Control Theory
6(2)
1.2.2 Multiple Performance Requirements
8(2)
1.2.3 Design Techniques for Nonlinear Stochastic Systems with Variance Constraints
10(2)
1.2.4 A Special Case of Multi-Objective Design: Mixed Control/Filtering
12(1)
1.3 Outline
13(6)
2 Robust H∞ Control with Variance Constraints
19(26)
2.1 Problem Formulation
20(2)
2.2 Stability, H∞ Performance, and Variance Analysis
22(7)
2.2.1 Stability and H∞ Performance Analysis
24(1)
2.2.2 Variance Analysis
25(4)
2.3 Robust Controller Design
29(5)
2.4 Numerical Example
34(10)
2.5 Summary
44(1)
3 Robust Mixed H2/H∞ Filtering
45(24)
3.1 System Description and Problem Formulation
46(3)
3.2 Algebraic Characterizations for Robust H2/H∞ Filtering
49(8)
3.2.1 Robust H2 Filtering
49(7)
3.2.2 Robust H∞ Filtering
56(1)
3.3 Robust H2/H∞ Filter Design Techniques
57(9)
3.4 An Illustrative Example
66(2)
3.5 Summary
68(1)
4 Robust Variance-Constrained Filtering with Missing Measurements
69(26)
4.1 Problem Formulation
70(4)
4.2 Stability and Variance Analysis
74(4)
4.3 Robust Filter Design
78(5)
4.4 Numerical Example
83(11)
4.5 Summary
94(1)
5 Robust Fault-Tolerant Control with Variance Constraints
95(18)
5.1 Problem Formulation
96(2)
5.2 Stability and Variance Analysis
98(3)
5.3 Robust Controller Design
101(6)
5.4 Numerical Example
107(5)
5.5 Summary
112(1)
6 Robust H2 Sliding Mode Control
113(22)
6.1 The System Model
114(1)
6.2 Robust H2 Sliding Mode Control
115(8)
6.2.1 Switching Surface
115(1)
6.2.2 Performances of the Sliding Motion
116(7)
6.2.3 Computational Algorithm
123(1)
6.3 Sliding Mode Controller
123(2)
6.4 Numerical Example
125(8)
6.5 Summary
133(2)
7 Variance-Constrained Dissipative Control with Degraded Measurements
135(24)
7.1 Problem Formulation
136(3)
7.2 Stability, Dissipativity, and Variance Analysis
139(6)
7.3 Observer-Based Controller Design
145(8)
7.3.1 Solvability of the Multi-Objective Control Problem
145(7)
7.3.2 Computational Algorithm
152(1)
7.4 Numerical Example
153(1)
7.5 Summary
154(5)
8 Variance-Constrained H∞ Control with Multiplicative Noises
159(16)
8.1 Problem Formulation
160(2)
8.2 Stability, H∞ Performance, and Variance Analysis
162(6)
8.2.1 Stability
162(2)
8.2.2 H∞ Performance
164(2)
8.2.3 Variance Analysis
166(2)
8.3 Robust State Feedback Controller Design
168(3)
8.4 Numerical Example
171(2)
8.5 Summary
173(2)
9 Robust H∞ Control with Variance Constraints: the Finite-Horizon Case
175(20)
9.1 Problem Formulation
176(2)
9.2 Performance Analysis
178(5)
9.2.1 H∞ Performance
178(2)
9.2.2 Variance Analysis
180(3)
9.3 Robust Finite-Horizon Controller Design
183(4)
9.4 Numerical Example
187(6)
9.5 Summary
193(2)
10 Error Variance-Constrained H∞ Filtering with Degraded Measurements: The Finite-Horizon Case
195(22)
10.1 Problem Formulation
196(3)
10.2 Performance Analysis
199(6)
10.2.1 H∞ Performance Analysis
199(5)
10.2.2 System Covariance Analysis
204(1)
10.3 Robust Filter Design
205(3)
10.4 Numerical Example
208(7)
10.5 Summary
215(2)
11 Mixed H2/H∞ Control with Randomly Occurring Nonlinearities: The Finite-Horizon Case
217(16)
11.1 Problem Formulation
219(2)
11.2 H∞ Performance
221(3)
11.3 Mixed H2/H∞ Controller Design
224(4)
11.3.1 State-Feedback Controller Design
224(3)
11.3.2 Computational Algorithm
227(1)
11.4 Numerical Example
228(4)
11.5 Summary
232(1)
12 Mixed H2/H∞ Control with Markovian Jump Parameters and Probabilistic Sensor Failures: The Finite-Horizon Case
233(18)
12.1 Problem Formulation
234(2)
12.2 H∞ Performance
236(4)
12.3 Mixed H2/H∞ Controller Design
240(4)
12.3.1 Controller Design
240(4)
12.3.2 Computational Algorithm
244(1)
12.4 Numerical Example
244(4)
12.5 Summary
248(3)
13 Robust Variance-Constrained H∞ Control with Randomly Occurring Sensor Failures: The Finite-Horizon Case
251(20)
13.1 Problem Formulation
252(4)
13.2 H∞ and Covariance Performance Analysis
256(7)
13.2.1 H∞ Performance
256(4)
13.2.2 Covariance Analysis
260(3)
13.3 Robust Finite-Horizon Controller Design
263(3)
13.3.1 Controller Design
263(2)
13.3.2 Computational Algorithm
265(1)
13.4 Numerical Example
266(3)
13.5 Summary
269(2)
14 Mixed H2/H∞ Control with Actuator Failures: the Finite-Horizon Case
271(14)
14.1 Problem Formulation
272(3)
14.2 H∞ Performance
275(2)
14.3 Multi-Objective Controller Design
277(5)
14.3.1 Controller Design
278(3)
14.3.2 Computational Algorithm
281(1)
14.4 Numerical Example
282(2)
14.5 Summary
284(1)
15 Conclusions and Future Topics
285(2)
15.1 Concluding Remarks
285(1)
15.2 Future Research
285(2)
References 287(8)
Index 295
Lifeng Ma received the Ph.D. degree in Control Science and Engineering in 2010 from Nanjing University of Science and Technology, Nanjing, China. From August 2008 to February 2009, he was a Visiting Scholar in the Department of Information Systems and Computing, Brunel University, London, UK. From January 2010 to March 2010 and from May 2011 to September 2011, he was a Research Associate in the Department of Mechanical Engineering, the University of Hong Kong. He is now a Lecturer with the School of Automation, Nanjing University of Science and Technology, Nanjing, China. Dr. Ma's current research interests include nonlinear control and stochastic control. He is a very active reviewer for many international journals.

Zidong Wang is currently Professor of Dynamical Systems and Computing in the Department of Information Systems and Computing, Brunel University, UK. From 1990 to 2002, he held teaching and research appointments in universities in China, Germany and the UK. Prof. Wang's research interests include dynamical systems, signal processing, bioinformatics, control theory and applications. He has published more than 280 papers in refereed international journals. He is a holder of the Alexander von Humboldt Research Fellowship of Germany, the JSPS Research Fellowship of Japan, William Mong Visiting Research Fellowship of Hong Kong. He serves as the Executive Editor for Systems Science and Control Engineering (Taylor and Francis) and an Associate Editor for 11 international journals including five IEEE Transactions. Prof. Wang is a Fellow of the IEEE, a Fellow of the Royal Statistical Society and a member of the program committee for many international conferences.

Yuming Bo received his BSc degree in Automatic Control in 1984, his MSc degree in Automatic Control in 1987 and PhD degree in Control Theory and Control Engineering in 2005, all from Nanjing University of Science and Technology, Nanjing, China. He is now a Professor of Control Theory and Control Engineering in the School of Automation at Nanjing University of Science and Technology, Nanjing, China. His research interests include stochastic control and estimation, computer communication and programming. He has published more than 20 papers in refereed journals and served as an associate editor for two journals.
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