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E-raamat: Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems: Analytical and Soft Computing Approaches

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Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems: Analytical and Soft Computing ApproachesSuurem pilt
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Taking the reader from advanced state estimation strategies right up to the latest techniques in soft computing, this book explains how to implement the Takagi-Sugeno models of fuzzy-systems fault diagnosis and fault-tolerant control in a unified framework.



This book presents selected fault diagnosis and fault-tolerant control strategies for non-linear systems in a unified framework. In particular, starting from advanced state estimation strategies up to modern soft computing, the discrete-time description of the system is employed Part I of the book presents original research results regarding state estimation and neural networks for robust fault diagnosis. Part II is devoted to the presentation of integrated fault diagnosis and fault-tolerant systems. It starts with a general fault-tolerant control framework, which is then extended by introducing robustness with respect to various uncertainties. Finally, it is shown how to implement the proposed framework for fuzzy systems described by the well-known Takagi–Sugeno models.

This research monograph is intended for researchers, engineers, and advanced postgraduate students in control and electrical engineering, computer science, as well as mechanical and chemical engineering.

1 Introduction
1(18)
1.1 Introductory Background
5(8)
1.2 Content
13(6)
References
15(4)
Part I Robust Fault Diagnosis
2 Unknown Input Observers and Filters
19(38)
2.1 Unknown Input Decoupling
21(1)
2.2 Preventing Fault Decoupling
22(3)
2.3 First- and Second-order Extended Unknown Input Observers
25(7)
2.3.1 Convergence Analysis
26(3)
2.3.2 Design Principles
29(3)
2.4 Unscented Kalman Filter
32(3)
2.4.1 Unscented Transform
33(1)
2.4.2 Principle of the UKF-Based UJF
34(1)
2.5 Determination of an Unknown Input Distribution Matrix
35(4)
2.6 Design of the UIF with Varying Unknown Input Distribution Matrices
39(3)
2.7 Illustrative Examples
42(12)
2.7.1 Estimation of E for an Induction Motor
43(3)
2.7.2 Varying E Case
46(1)
2.7.3 Fault Detection and Isolation of a Two-Tank System
47(6)
2.7.4 First-Versus Second-order EUIO
53(1)
2.8 Concluding Remarks
54(3)
References
55(2)
3 Neural Network-Based Approaches to Fault Diagnosis
57(62)
3.1 Robust Fault Detection with the Multi-Layer Perceptron
58(25)
3.1.1 Illustrative Example
63(2)
3.1.2 Algorithms and Properties of D-OED for Neural Networks
65(13)
3.1.3 Industrial Application
78(5)
3.2 GMDH Neural Networks
83(28)
3.2.1 Model Uncertainty in the GMDH Neural Network
84(2)
3.2.2 Bounded-Error Approach
86(6)
3.2.3 Synthesis of the GMDH Neural Network Via the BEA
92(1)
3.2.4 Robust Fault Detection with the GMDH Model
93(2)
3.2.5 Alternative Robust Fault Detection Procedure: A Backward Detection Test
95(6)
3.2.6 Industrial Application
101(10)
3.3 Concluding Remarks
111(8)
References
113(6)
Part II Integrated Fault Diagnosis and Control
4 Integrated Fault Diagnosis and Control: Principles and Design Strategies
119(24)
4.1 FTC Strategy
120(5)
4.1.1 Fault Identification
121(1)
4.1.2 Stabilisation Problem
122(1)
4.1.3 Observer Design
123(1)
4.1.4 Integrated Design Procedure
123(2)
4.2 Extension to Non-Linear Systems
125(5)
4.3 Constrained State Estimation
130(2)
4.3.1 Complete Design Procedure
131(1)
4.4 Application Example
132(8)
4.4.1 Description of the Twin-Rotor MIMO System
132(1)
4.4.2 Non-Linear Reference Model of Twin-Rotor MIMO System
133(2)
4.4.3 Fault Scenario 1
135(1)
4.4.4 Fault Scenario 2
136(2)
4.4.5 Fault Scenario 3
138(2)
4.5 Concluding Remarks
140(3)
References
140(3)
5 Robust H∞-Based Approaches
143(46)
5.1 Towards Robust Fault-Tolerant Control
143(10)
5.1.1 Preliminaries
143(2)
5.1.2 Fault Estimation Approach
145(4)
5.1.3 Integrated FTC Design
149(2)
5.1.4 Illustrative Example: Fault Estimation
151(2)
5.2 Complete Robust Design of Fault-Tolerant Control
153(15)
5.2.1 Preliminaries
154(1)
5.2.2 Fault Estimation Strategy
154(6)
5.2.3 Guaranteed Decay Rate
160(2)
5.2.4 Integrated FTC Design
162(2)
5.2.5 Illustrative Example: Fault Estimation
164(2)
5.2.6 Illustrative Examples: Fault-Tolerant Control
166(2)
5.3 Robust Design for Quasi-LPV Systems
168(18)
5.3.1 Actuator Fault Estimation
168(9)
5.3.2 Sensor Fault Estimation
177(2)
5.3.3 Integrated FTC Design
179(1)
5.3.4 Illustrative Example: Fault Estimation of a Multi-Tank System
180(6)
5.4 Concluding Remarks
186(3)
References
187(2)
6 Fuzzy Multiple-Model Approach to Fault-Tolerant Control
189(32)
6.1 Essentials of Fuzzy Logic
189(1)
6.2 Fuzzy Multiple-Model Representation
190(2)
6.3 Development of the Takagi-Sugeno Fuzzy Model
192(1)
6.4 Virtual Fuzzy Actuators
193(6)
6.4.1 Implementation Details
198(1)
6.5 Virtual Fuzzy Sensor
199(1)
6.6 Illustrative Examples
200(18)
6.6.1 TS Model Design for a Tunnel Furnace
201(4)
6.6.2 Virtual Sensor for a Tunnel Furnace
205(2)
6.6.3 FTC of a Three-Tank System
207(5)
6.6.4 FTC of a Twin-Rotor System
212(6)
6.7 Concluding Remarks
218(3)
References
219(2)
7 Conclusions and Future Research Directions
221(6)
Reference
225(2)
Index 227
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