Magdi S. Mahmoud, Yuanqing Xia
Analysis and Synthesis of Fault-Tolerant Control Systems (eBook, ePUB)
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Magdi S. Mahmoud, Yuanqing Xia
Analysis and Synthesis of Fault-Tolerant Control Systems (eBook, ePUB)
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In recent years, control systems have become more sophisticated in order to meet increased performance and safety requirements for modern technological systems. Engineers are becoming more aware that conventional feedback control design for a complex system may result in unsatisfactory performance, or even instability, in the event of malfunctions in actuators, sensors or other system components. In order to circumvent such weaknesses, new approaches to control system design have emerged which can tolerate component malfunctions while maintaining acceptable stability and performance. These…mehr
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In recent years, control systems have become more sophisticated in order to meet increased performance and safety requirements for modern technological systems. Engineers are becoming more aware that conventional feedback control design for a complex system may result in unsatisfactory performance, or even instability, in the event of malfunctions in actuators, sensors or other system components. In order to circumvent such weaknesses, new approaches to control system design have emerged which can tolerate component malfunctions while maintaining acceptable stability and performance. These types of control systems are often known as fault-tolerant control systems (FTCS). More precisely, FTCS are control systems which possess the ability to accommodate component failure automatically. Analysis and Synthesis of Fault-Tolerant Control Systems comprehensively covers the analysis and synthesis methods of fault tolerant control systems. It unifies the methods for developing controllers and filters for a wide class of dynamical systems and reports on the recent technical advances in design methodologies. MATLAB® is used throughout the book, to demonstrate methods of analysis and design. Key features: * Provides advanced theoretical methods and typical practical applications * Provides access to a spectrum of control design methods applied to industrial systems * Includes case studies and illustrative examples * Contains end-of-chapter problems Analysis and Synthesis of Fault-Tolerant Control Systems is a comprehensive reference for researchers and practitioners working in this area, and is also a valuable source of information for graduates and senior undergraduates in control, mechanical, aerospace, electrical and mechatronics engineering departments.
Produktdetails
- Produktdetails
- Verlag: John Wiley & Sons
- Seitenzahl: 480
- Erscheinungstermin: 28. Oktober 2013
- Englisch
- ISBN-13: 9781118700358
- Artikelnr.: 39937321
- Verlag: John Wiley & Sons
- Seitenzahl: 480
- Erscheinungstermin: 28. Oktober 2013
- Englisch
- ISBN-13: 9781118700358
- Artikelnr.: 39937321
MagdiSadek Mahmoud obtained Ph. D. in systems engineering from Cairo University, 1974. He has been a professor of engineering since 1984. He is now a Distinguished University Professor at KFUPM, Saudi Arabia. He worked at different universities world-wide including Egypt, Kuwait, UAE, UK, USA, Singapore and Australia.He lectured in Venezuela, Germany, UK, USA, Canada and China. He has been actively engaged in teaching and research in the development of modern methodologies to distributed control and filtering, switched time-delay systems, fault-tolerant systems and information technology. He is the principal author of thirty (30) books, inclusive book-chapters and the author/co-author of more than 500 peer-reviewed papers. He is the recipient of two national, one regional and four university prizes for outstanding research in engineering. He is a fellow of the IEE, a senior member of the IEEE, the CEI (UK), and a registered consultant engineer of information engineering and systems (Egypt). Email: href="mailto:magdim@yahoo.com">magdim@yahoo.com, href="mailto:msmahmoud@kfupm.edu.sa">msmahmoud@kfupm.edu.sa, Website: href="http://faculty.kfupm.edu.sa/se/msmahmoud/">http://faculty.kfupm.edu.sa/se/msmahmoud/ Dr. Yuanqing Xia received his M.S. degree in Fundamental Mathematics from Anhui University, China, in 1998 and his Ph.D. Degree in Control Theory and Control Engineering from Beijing University of Aeronautics and Astronautics, Beijing, China, in 2001. From 1991-1995, he was with Tongcheng Middle-School, Anhui, China, where he worked as a teacher. During January 2002-November 2003, he was a postdoctoral research associate in the Institute of Systems Science, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing, China, where he worked on navigation, guidance and control. From November 2003 to February 2004, he was with the National University of Singapore as a research fellow, where he worked on variable structure control. From February 2004 to February 2006, he was with the University of Glamorgan, Pontypridd, U.K., as a Research Fellow, where he worked on networked control systems. From February 2007 to June 2008, he was a Guest Professor with Innsbruck Medical University, Innsbruck, Austria, where he worked on biomedical signal processing. Since July 2004, he has been with the Department of Automatic Control, Beijing Institute of Technology, Beijing, first as an Associate Professor, then, since 2008, as a Professor and in 2012. He has published five monographs in Springer and more than 100 papers in journals. He was appointed as ``Xu Teli" distinguished professor in Beijing Institute of Technology and obtained National Science Foundation for Distinguished Young Scholars of China. He was obtained Second Award of Beijing Municipal Science and Technology (No.1) in 2010 , Second National Award for Science and Technology (No.2) in 2011, and second natural science award of The Ministry of Education in 2012. He is an editor in deputy of Journal of Beijing Institute of Technology, associate editor of Acta Automatica Sinica, International Journal of Innovative Computing, Information and Control.
Preface xv Acknowledgments xvii 1 Introduction 1 1.1 Overview 1 1.2 Basic
Concepts of Faults 2 1.3 Classification of Fault Detection Methods 3 1.3.1
Hardware redundancy based fault detection 3 1.3.2 Plausibility test 3 1.3.3
Signal-based fault diagnosis 4 1.3.4 Model-based fault detection 5 1.4
Types of Fault-Tolerant Control System 8 1.5 Objectives and Structure of
AFTCS 8 1.6 Classification of Reconfigurable Control Methods 10 1.6.1
Classification based on control algorithms 10 1.6.2 Classification based on
field of application 11 1.7 Outline of the Book 11 1.7.1 Methodology 11
1.7.2 Chapter organization 12 1.8 Notes 13 References 13 2 Fault Diagnosis
and Detection 17 2.1 Introduction 17 2.2 Related Work 17 2.2.1 Model-based
schemes 17 2.2.2 Model-free schemes 18 2.2.3 Probabilistic schemes 19 2.3
Integrated Approach 19 2.3.1 Improved multi-sensor data fusion 19 2.3.2
Unscented transformation 21 2.3.3 Unscented Kalman filter 22 2.3.4
Parameter estimation 23 2.3.5 Multi-sensor integration architectures 24 2.4
Robust Unscented Kalman Filter 26 2.4.1 Introduction 26 2.4.2 Problem
formulation 28 2.4.3 Residual generation 29 2.4.4 Residual evaluation 29
2.5 Quadruple Tank System 30 2.5.1 Model of the QTS 31 2.5.2 Fault
scenarios in QTS 32 2.5.3 Implementation structure of UKF 33 2.5.4 UKF with
centralized multi-sensor data fusion 35 2.5.5 UKF with decentralized
multi-sensor data fusion 35 2.5.6 Drift detection 35 2.6 Industrial Utility
Boiler 38 2.6.1 Steam flow dynamics 38 2.6.2 Drum pressure dynamics 40
2.6.3 Drum level dynamics 40 2.6.4 Steam temperature 41 2.6.5 Fault model
for the utility boiler 42 2.6.6 Fault scenarios in the utility boiler 43
2.6.7 UKF with centralized multi-sensor data fusion 43 2.6.8 UKF with
decentralized multi-sensor data fusion 43 2.6.9 Drift detection 45 2.6.10
Remarks 45 2.7 Notes 46 References 46 3 Robust Fault Detection 49 3.1
Distributed Fault Diagnosis 49 3.1.1 Introduction 49 3.1.2 System model 50
3.1.3 Distributed FDI architecture 55 3.1.4 Distributed fault detection
method 55 3.1.5 Adaptive thresholds 57 3.1.6 Distributed fault isolation
method 62 3.1.7 Adaptive thresholds for DFDI 64 3.1.8 Fault detectability
condition 67 3.1.9 Fault isolability analysis 69 3.1.10 Stability and
learning capability 71 3.2 Robust Fault Detection Filters 74 3.2.1
Reference model 74 3.2.2 Design of adaptive threshold 76 3.2.3 Iterative
update of noise mean and covariance 77 3.2.4 Unscented transformation (UT)
79 3.2.5 Car-like mobile robot application 82 3.3 Simultaneous Fault
Detection and Control 90 3.3.1 Introduction 93 3.3.2 System model 93 3.3.3
Problem formulation 95 3.3.4 Simultaneous fault detection and control
problem 96 3.3.5 Two-tank system simulation 106 3.4 Data-Driven Fault
Detection Design 108 3.4.1 Introduction 109 3.4.2 Problem formulation 111
3.4.3 Selection of weighting matrix 112 3.4.4 Design of FDF for time-delay
system 113 3.4.5 LMI design approach 114 3.4.6 Four-tank system simulation
119 3.5 Robust Adaptive Fault Estimation 122 3.5.1 Introduction 124 3.5.2
Problem statement 125 3.5.3 Adaptive observer 127 3.6 Notes 131 References
131 4 Fault-Tolerant Control Systems 135 4.1 Model Prediction-Based Design
Approach 135 4.1.1 Introduction 135 4.1.2 System description 136 4.1.3
Discrete-time UKF 138 4.1.4 Unscented Transformation (UT) 141 4.1.5
Controller reconfiguration 143 4.1.6 Model predictive control 144 4.1.7
Interconnected CSTR units 149 4.1.8 Four-tank system 151 4.1.9 Simulation
results 152 4.1.10 Drift detection in the interconnected CSTRs 152 4.1.11
Information fusion from UKF 152 4.1.12 Drift detection in the four-tank
system 156 4.2 Observer-Based Active Structures 160 4.2.1 Problem statement
160 4.2.2 A separation principle 162 4.2.3 FDI residuals 164 4.2.4 Control
of integrity 164 4.2.5 Overall stability 165 4.2.6 Design outline 165 4.2.7
Design of an active FTC scheme 166 4.2.8 Extraction of FDI-FTC pairs 166
4.2.9 Simulation 169 4.3 Notes 172 References 172 5 Fault-Tolerant
Nonlinear Control Systems 175 5.1 Comparison of Fault Detection Schemes 175
5.2 Fault Detection in Nonlinear Systems 176 5.3 Nonlinear Observer-Based
Residual Generation Schemes 176 5.3.1 General considerations 176 5.3.2
Extended Luenberger observer 177 5.3.3 Nonlinear identity observer approach
177 5.3.4 Unknown input observer approach 178 5.3.5 The disturbance
decoupling nonlinear observer approach 178 5.3.6 Adaptive nonlinear
observer approach 178 5.3.7 High-gain observer approach 178 5.3.8
Sliding-mode observer approach 178 5.3.9 Geometric approach 179 5.3.10
Game-theoretic approach 179 5.3.11 Observers for Lipschitz nonlinear
systems 179 5.3.12 Lyapunov-reconstruction-based passive scheme 180 5.3.13
Time-varying results 185 5.3.14 Optimization-based active scheme 187 5.3.15
Learning-based active scheme 190 5.3.16 Adaptive backstepping-based active
scheme 191 5.3.17 Switched control-based active scheme 193 5.3.18
Predictive control-based active scheme 195 5.4 Integrated Control
Reconfiguration Scheme 197 5.4.1 Introduction 197 5.4.2 Basic features 198
5.4.3 Nonlinear model of a pendulum on a cart 199 5.4.4 NGA adaptive filter
design 201 5.4.5 Simulation results 207 5.4.6 Performance evaluation 209
5.4.7 Comparative studies 211 5.5 Notes 215 References 215 6 Robust Fault
Estimation 219 6.1 Introduction 219 6.2 System Description 220 6.3
Multiconstrained Fault Estimation 221 6.3.1 Observer design 221 6.3.2
Existence conditions 226 6.3.3 Improved results 228 6.3.4 Simulation
results 232 6.4 Adaptive Fault Estimation 235 6.4.1 Introduction 236 6.4.2
Problem statement 238 6.4.3 Robust adaptive estimation 239 6.4.4 Internal
stability analysis 240 6.4.5 Robust performance index 241 6.4.6 Simulation
242 6.5 Adaptive Tracking Control Scheme 244 6.5.1 Attitude dynamics 244
6.5.2 Fault detection scheme 248 6.5.3 Fault-tolerant tracking scheme 250
6.6 Notes 254 References 254 7 Fault Detection of Networked Control Systems
257 7.1 Introduction 257 7.2 Problem Formulation 258 7.3 Modified Residual
Generator Scheme 259 7.3.1 Modified residual generator and dynamic analysis
259 7.3.2 Residual evaluation 261 7.3.3 Co-design of residual generator and
evaluation 264 7.4 Quantized Fault-Tolerant Control 267 7.4.1 Introduction
267 7.4.2 Problem statement 268 7.4.3 Quantized control design 271 7.4.4
Simulation 276 7.5 Sliding-Mode Observer 278 7.5.1 Introduction 278 7.5.2
Dynamic model 280 7.5.3 Limited state measurements 286 7.5.4 Simulation
results: full state measurements 290 7.5.5 Simulation results: partial
state measurements 293 7.6 Control of Linear Switched Systems 294 7.6.1
Introduction 295 7.6.2 Problem formulation 295 7.6.3 Stability of a
closed-loop system 296 7.6.4 Simulation 300 7.7 Notes 303 References 303 8
Industrial Fault-Tolerant Architectures 307 8.1 Introduction 307 8.2 System
Architecture 308 8.3 Architecture of a Fault-Tolerant Node 309 8.3.1 Basic
architecture 309 8.3.2 Architecture with improved reliability 310 8.3.3
Symmetric node architecture 310 8.3.4 Results 311 8.4 Recovery Points 312
8.5 Networks 314 8.6 System Fault Injection and Monitoring 315 8.6.1
Monitoring systems 315 8.6.2 Design methodology 316 8.7 Notes 318
References 319 9 Fault Estimation for Stochastic Systems 321 9.1
Introduction 321 9.2 Actuator Fault Diagnosis Design 322 9.3 Fault-Tolerant
Controller Design 324 9.4 Extension to an Unknown Input Case 325 9.5
Aircraft Application 326 9.5.1 Transforming the system into standard form
327 9.5.2 Simulation results 329 9.6 Router Fault Accommodation in Real
Time 330 9.6.1 Canonical controller and achievable behavior 333 9.6.2
Router modeling and desired behavior 334 9.6.3 Description of fault
behavior 336 9.6.4 A least restrictive controller 338 9.7 Fault Detection
for Markov Jump Systems 338 9.7.1 Introduction 339 9.7.2 Problem
formulation 340 9.7.3 H infinity bounded real lemmas 343 9.7.4 H infinity
FD filter design 345 9.7.5 Simulation 347 9.8 Notes 352 References 353 10
Applications 355 10.1 Detection of Abrupt Changes in an Electrocardiogram
355 10.1.1 Introduction 355 10.1.2 Modeling ECG signals with an AR model
356 10.1.3 Linear models with additive abrupt changes 358 10.1.4 Off-line
detection of abrupt changes in ECG 361 10.1.5 Online detection of abrupt
changes in ECG 363 10.2 Detection of Abrupt Changes in the Frequency Domain
365 10.2.1 Introduction 365 10.2.2 Problem formulation 366 10.2.3 Frequency
domain ML ratio estimation 368 10.2.4 Likelihood of the hypothesis of no
abrupt change 372 10.2.5 Effect of an abrupt change 374 10.2.6 Simulation
results 382 10.3 Electromechanical Positioning System 383 10.3.1
Introduction 383 10.3.2 Problem formulation 385 10.3.3 Test bed 386 10.4
Application to Fermentation Processes 387 10.4.1 Nonlinear faulty dynamic
system 388 10.4.2 Residual characteristics 389 10.4.3 The parameter filter
399 10.4.4 Fault filter 400 10.4.5 Fault isolation and identification 401
10.4.6 Isolation speed 401 10.4.7 Parameter partition 402 10.4.8 Adaptive
intervals 402 10.4.9 Simulation studies 405 10.5 Flexible-Joint Robots 415
10.5.1 Problem formulation 415 10.5.2 Fault detection scheme 417 10.5.3
Adaptive fault accommodation control 420 10.5.4 Control with prescribed
performance bounds 422 10.5.5 Simulation results 425 10.6 Notes 429
References 430 A Supplementary Information 435 A.1 Notation 435 A.1.1
Kronecker products 436 A.1.2 Some definitions 437 A.1.3 Matrix lemmas 438
A.2 Results from Probability Theory 440 A.2.1 Results-A 440 A.2.2 Results-B
441 A.2.3 Results-C 441 A.2.4 Minimum mean square estimate 442 A.3
Stability Notions 444 A.3.1 Practical stabilizability 444 A.3.2 Razumikhin
stability 445 A.4 Basic Inequalities 447 A.4.1 Schur complements 447 A.4.2
Bounding inequalities 449 A.5 Linear Matrix Inequalities 453 A.5.1 Basics
453 A.5.2 Some standard problems 454 A.5.3 The S-procedure 455 A.6 Some
Formulas on Matrix Inverses 456 A.6.1 Inverse of block matrices 456 A.6.2
Matrix inversion lemma 457 References 458 Index 459
Concepts of Faults 2 1.3 Classification of Fault Detection Methods 3 1.3.1
Hardware redundancy based fault detection 3 1.3.2 Plausibility test 3 1.3.3
Signal-based fault diagnosis 4 1.3.4 Model-based fault detection 5 1.4
Types of Fault-Tolerant Control System 8 1.5 Objectives and Structure of
AFTCS 8 1.6 Classification of Reconfigurable Control Methods 10 1.6.1
Classification based on control algorithms 10 1.6.2 Classification based on
field of application 11 1.7 Outline of the Book 11 1.7.1 Methodology 11
1.7.2 Chapter organization 12 1.8 Notes 13 References 13 2 Fault Diagnosis
and Detection 17 2.1 Introduction 17 2.2 Related Work 17 2.2.1 Model-based
schemes 17 2.2.2 Model-free schemes 18 2.2.3 Probabilistic schemes 19 2.3
Integrated Approach 19 2.3.1 Improved multi-sensor data fusion 19 2.3.2
Unscented transformation 21 2.3.3 Unscented Kalman filter 22 2.3.4
Parameter estimation 23 2.3.5 Multi-sensor integration architectures 24 2.4
Robust Unscented Kalman Filter 26 2.4.1 Introduction 26 2.4.2 Problem
formulation 28 2.4.3 Residual generation 29 2.4.4 Residual evaluation 29
2.5 Quadruple Tank System 30 2.5.1 Model of the QTS 31 2.5.2 Fault
scenarios in QTS 32 2.5.3 Implementation structure of UKF 33 2.5.4 UKF with
centralized multi-sensor data fusion 35 2.5.5 UKF with decentralized
multi-sensor data fusion 35 2.5.6 Drift detection 35 2.6 Industrial Utility
Boiler 38 2.6.1 Steam flow dynamics 38 2.6.2 Drum pressure dynamics 40
2.6.3 Drum level dynamics 40 2.6.4 Steam temperature 41 2.6.5 Fault model
for the utility boiler 42 2.6.6 Fault scenarios in the utility boiler 43
2.6.7 UKF with centralized multi-sensor data fusion 43 2.6.8 UKF with
decentralized multi-sensor data fusion 43 2.6.9 Drift detection 45 2.6.10
Remarks 45 2.7 Notes 46 References 46 3 Robust Fault Detection 49 3.1
Distributed Fault Diagnosis 49 3.1.1 Introduction 49 3.1.2 System model 50
3.1.3 Distributed FDI architecture 55 3.1.4 Distributed fault detection
method 55 3.1.5 Adaptive thresholds 57 3.1.6 Distributed fault isolation
method 62 3.1.7 Adaptive thresholds for DFDI 64 3.1.8 Fault detectability
condition 67 3.1.9 Fault isolability analysis 69 3.1.10 Stability and
learning capability 71 3.2 Robust Fault Detection Filters 74 3.2.1
Reference model 74 3.2.2 Design of adaptive threshold 76 3.2.3 Iterative
update of noise mean and covariance 77 3.2.4 Unscented transformation (UT)
79 3.2.5 Car-like mobile robot application 82 3.3 Simultaneous Fault
Detection and Control 90 3.3.1 Introduction 93 3.3.2 System model 93 3.3.3
Problem formulation 95 3.3.4 Simultaneous fault detection and control
problem 96 3.3.5 Two-tank system simulation 106 3.4 Data-Driven Fault
Detection Design 108 3.4.1 Introduction 109 3.4.2 Problem formulation 111
3.4.3 Selection of weighting matrix 112 3.4.4 Design of FDF for time-delay
system 113 3.4.5 LMI design approach 114 3.4.6 Four-tank system simulation
119 3.5 Robust Adaptive Fault Estimation 122 3.5.1 Introduction 124 3.5.2
Problem statement 125 3.5.3 Adaptive observer 127 3.6 Notes 131 References
131 4 Fault-Tolerant Control Systems 135 4.1 Model Prediction-Based Design
Approach 135 4.1.1 Introduction 135 4.1.2 System description 136 4.1.3
Discrete-time UKF 138 4.1.4 Unscented Transformation (UT) 141 4.1.5
Controller reconfiguration 143 4.1.6 Model predictive control 144 4.1.7
Interconnected CSTR units 149 4.1.8 Four-tank system 151 4.1.9 Simulation
results 152 4.1.10 Drift detection in the interconnected CSTRs 152 4.1.11
Information fusion from UKF 152 4.1.12 Drift detection in the four-tank
system 156 4.2 Observer-Based Active Structures 160 4.2.1 Problem statement
160 4.2.2 A separation principle 162 4.2.3 FDI residuals 164 4.2.4 Control
of integrity 164 4.2.5 Overall stability 165 4.2.6 Design outline 165 4.2.7
Design of an active FTC scheme 166 4.2.8 Extraction of FDI-FTC pairs 166
4.2.9 Simulation 169 4.3 Notes 172 References 172 5 Fault-Tolerant
Nonlinear Control Systems 175 5.1 Comparison of Fault Detection Schemes 175
5.2 Fault Detection in Nonlinear Systems 176 5.3 Nonlinear Observer-Based
Residual Generation Schemes 176 5.3.1 General considerations 176 5.3.2
Extended Luenberger observer 177 5.3.3 Nonlinear identity observer approach
177 5.3.4 Unknown input observer approach 178 5.3.5 The disturbance
decoupling nonlinear observer approach 178 5.3.6 Adaptive nonlinear
observer approach 178 5.3.7 High-gain observer approach 178 5.3.8
Sliding-mode observer approach 178 5.3.9 Geometric approach 179 5.3.10
Game-theoretic approach 179 5.3.11 Observers for Lipschitz nonlinear
systems 179 5.3.12 Lyapunov-reconstruction-based passive scheme 180 5.3.13
Time-varying results 185 5.3.14 Optimization-based active scheme 187 5.3.15
Learning-based active scheme 190 5.3.16 Adaptive backstepping-based active
scheme 191 5.3.17 Switched control-based active scheme 193 5.3.18
Predictive control-based active scheme 195 5.4 Integrated Control
Reconfiguration Scheme 197 5.4.1 Introduction 197 5.4.2 Basic features 198
5.4.3 Nonlinear model of a pendulum on a cart 199 5.4.4 NGA adaptive filter
design 201 5.4.5 Simulation results 207 5.4.6 Performance evaluation 209
5.4.7 Comparative studies 211 5.5 Notes 215 References 215 6 Robust Fault
Estimation 219 6.1 Introduction 219 6.2 System Description 220 6.3
Multiconstrained Fault Estimation 221 6.3.1 Observer design 221 6.3.2
Existence conditions 226 6.3.3 Improved results 228 6.3.4 Simulation
results 232 6.4 Adaptive Fault Estimation 235 6.4.1 Introduction 236 6.4.2
Problem statement 238 6.4.3 Robust adaptive estimation 239 6.4.4 Internal
stability analysis 240 6.4.5 Robust performance index 241 6.4.6 Simulation
242 6.5 Adaptive Tracking Control Scheme 244 6.5.1 Attitude dynamics 244
6.5.2 Fault detection scheme 248 6.5.3 Fault-tolerant tracking scheme 250
6.6 Notes 254 References 254 7 Fault Detection of Networked Control Systems
257 7.1 Introduction 257 7.2 Problem Formulation 258 7.3 Modified Residual
Generator Scheme 259 7.3.1 Modified residual generator and dynamic analysis
259 7.3.2 Residual evaluation 261 7.3.3 Co-design of residual generator and
evaluation 264 7.4 Quantized Fault-Tolerant Control 267 7.4.1 Introduction
267 7.4.2 Problem statement 268 7.4.3 Quantized control design 271 7.4.4
Simulation 276 7.5 Sliding-Mode Observer 278 7.5.1 Introduction 278 7.5.2
Dynamic model 280 7.5.3 Limited state measurements 286 7.5.4 Simulation
results: full state measurements 290 7.5.5 Simulation results: partial
state measurements 293 7.6 Control of Linear Switched Systems 294 7.6.1
Introduction 295 7.6.2 Problem formulation 295 7.6.3 Stability of a
closed-loop system 296 7.6.4 Simulation 300 7.7 Notes 303 References 303 8
Industrial Fault-Tolerant Architectures 307 8.1 Introduction 307 8.2 System
Architecture 308 8.3 Architecture of a Fault-Tolerant Node 309 8.3.1 Basic
architecture 309 8.3.2 Architecture with improved reliability 310 8.3.3
Symmetric node architecture 310 8.3.4 Results 311 8.4 Recovery Points 312
8.5 Networks 314 8.6 System Fault Injection and Monitoring 315 8.6.1
Monitoring systems 315 8.6.2 Design methodology 316 8.7 Notes 318
References 319 9 Fault Estimation for Stochastic Systems 321 9.1
Introduction 321 9.2 Actuator Fault Diagnosis Design 322 9.3 Fault-Tolerant
Controller Design 324 9.4 Extension to an Unknown Input Case 325 9.5
Aircraft Application 326 9.5.1 Transforming the system into standard form
327 9.5.2 Simulation results 329 9.6 Router Fault Accommodation in Real
Time 330 9.6.1 Canonical controller and achievable behavior 333 9.6.2
Router modeling and desired behavior 334 9.6.3 Description of fault
behavior 336 9.6.4 A least restrictive controller 338 9.7 Fault Detection
for Markov Jump Systems 338 9.7.1 Introduction 339 9.7.2 Problem
formulation 340 9.7.3 H infinity bounded real lemmas 343 9.7.4 H infinity
FD filter design 345 9.7.5 Simulation 347 9.8 Notes 352 References 353 10
Applications 355 10.1 Detection of Abrupt Changes in an Electrocardiogram
355 10.1.1 Introduction 355 10.1.2 Modeling ECG signals with an AR model
356 10.1.3 Linear models with additive abrupt changes 358 10.1.4 Off-line
detection of abrupt changes in ECG 361 10.1.5 Online detection of abrupt
changes in ECG 363 10.2 Detection of Abrupt Changes in the Frequency Domain
365 10.2.1 Introduction 365 10.2.2 Problem formulation 366 10.2.3 Frequency
domain ML ratio estimation 368 10.2.4 Likelihood of the hypothesis of no
abrupt change 372 10.2.5 Effect of an abrupt change 374 10.2.6 Simulation
results 382 10.3 Electromechanical Positioning System 383 10.3.1
Introduction 383 10.3.2 Problem formulation 385 10.3.3 Test bed 386 10.4
Application to Fermentation Processes 387 10.4.1 Nonlinear faulty dynamic
system 388 10.4.2 Residual characteristics 389 10.4.3 The parameter filter
399 10.4.4 Fault filter 400 10.4.5 Fault isolation and identification 401
10.4.6 Isolation speed 401 10.4.7 Parameter partition 402 10.4.8 Adaptive
intervals 402 10.4.9 Simulation studies 405 10.5 Flexible-Joint Robots 415
10.5.1 Problem formulation 415 10.5.2 Fault detection scheme 417 10.5.3
Adaptive fault accommodation control 420 10.5.4 Control with prescribed
performance bounds 422 10.5.5 Simulation results 425 10.6 Notes 429
References 430 A Supplementary Information 435 A.1 Notation 435 A.1.1
Kronecker products 436 A.1.2 Some definitions 437 A.1.3 Matrix lemmas 438
A.2 Results from Probability Theory 440 A.2.1 Results-A 440 A.2.2 Results-B
441 A.2.3 Results-C 441 A.2.4 Minimum mean square estimate 442 A.3
Stability Notions 444 A.3.1 Practical stabilizability 444 A.3.2 Razumikhin
stability 445 A.4 Basic Inequalities 447 A.4.1 Schur complements 447 A.4.2
Bounding inequalities 449 A.5 Linear Matrix Inequalities 453 A.5.1 Basics
453 A.5.2 Some standard problems 454 A.5.3 The S-procedure 455 A.6 Some
Formulas on Matrix Inverses 456 A.6.1 Inverse of block matrices 456 A.6.2
Matrix inversion lemma 457 References 458 Index 459
Preface xv Acknowledgments xvii 1 Introduction 1 1.1 Overview 1 1.2 Basic
Concepts of Faults 2 1.3 Classification of Fault Detection Methods 3 1.3.1
Hardware redundancy based fault detection 3 1.3.2 Plausibility test 3 1.3.3
Signal-based fault diagnosis 4 1.3.4 Model-based fault detection 5 1.4
Types of Fault-Tolerant Control System 8 1.5 Objectives and Structure of
AFTCS 8 1.6 Classification of Reconfigurable Control Methods 10 1.6.1
Classification based on control algorithms 10 1.6.2 Classification based on
field of application 11 1.7 Outline of the Book 11 1.7.1 Methodology 11
1.7.2 Chapter organization 12 1.8 Notes 13 References 13 2 Fault Diagnosis
and Detection 17 2.1 Introduction 17 2.2 Related Work 17 2.2.1 Model-based
schemes 17 2.2.2 Model-free schemes 18 2.2.3 Probabilistic schemes 19 2.3
Integrated Approach 19 2.3.1 Improved multi-sensor data fusion 19 2.3.2
Unscented transformation 21 2.3.3 Unscented Kalman filter 22 2.3.4
Parameter estimation 23 2.3.5 Multi-sensor integration architectures 24 2.4
Robust Unscented Kalman Filter 26 2.4.1 Introduction 26 2.4.2 Problem
formulation 28 2.4.3 Residual generation 29 2.4.4 Residual evaluation 29
2.5 Quadruple Tank System 30 2.5.1 Model of the QTS 31 2.5.2 Fault
scenarios in QTS 32 2.5.3 Implementation structure of UKF 33 2.5.4 UKF with
centralized multi-sensor data fusion 35 2.5.5 UKF with decentralized
multi-sensor data fusion 35 2.5.6 Drift detection 35 2.6 Industrial Utility
Boiler 38 2.6.1 Steam flow dynamics 38 2.6.2 Drum pressure dynamics 40
2.6.3 Drum level dynamics 40 2.6.4 Steam temperature 41 2.6.5 Fault model
for the utility boiler 42 2.6.6 Fault scenarios in the utility boiler 43
2.6.7 UKF with centralized multi-sensor data fusion 43 2.6.8 UKF with
decentralized multi-sensor data fusion 43 2.6.9 Drift detection 45 2.6.10
Remarks 45 2.7 Notes 46 References 46 3 Robust Fault Detection 49 3.1
Distributed Fault Diagnosis 49 3.1.1 Introduction 49 3.1.2 System model 50
3.1.3 Distributed FDI architecture 55 3.1.4 Distributed fault detection
method 55 3.1.5 Adaptive thresholds 57 3.1.6 Distributed fault isolation
method 62 3.1.7 Adaptive thresholds for DFDI 64 3.1.8 Fault detectability
condition 67 3.1.9 Fault isolability analysis 69 3.1.10 Stability and
learning capability 71 3.2 Robust Fault Detection Filters 74 3.2.1
Reference model 74 3.2.2 Design of adaptive threshold 76 3.2.3 Iterative
update of noise mean and covariance 77 3.2.4 Unscented transformation (UT)
79 3.2.5 Car-like mobile robot application 82 3.3 Simultaneous Fault
Detection and Control 90 3.3.1 Introduction 93 3.3.2 System model 93 3.3.3
Problem formulation 95 3.3.4 Simultaneous fault detection and control
problem 96 3.3.5 Two-tank system simulation 106 3.4 Data-Driven Fault
Detection Design 108 3.4.1 Introduction 109 3.4.2 Problem formulation 111
3.4.3 Selection of weighting matrix 112 3.4.4 Design of FDF for time-delay
system 113 3.4.5 LMI design approach 114 3.4.6 Four-tank system simulation
119 3.5 Robust Adaptive Fault Estimation 122 3.5.1 Introduction 124 3.5.2
Problem statement 125 3.5.3 Adaptive observer 127 3.6 Notes 131 References
131 4 Fault-Tolerant Control Systems 135 4.1 Model Prediction-Based Design
Approach 135 4.1.1 Introduction 135 4.1.2 System description 136 4.1.3
Discrete-time UKF 138 4.1.4 Unscented Transformation (UT) 141 4.1.5
Controller reconfiguration 143 4.1.6 Model predictive control 144 4.1.7
Interconnected CSTR units 149 4.1.8 Four-tank system 151 4.1.9 Simulation
results 152 4.1.10 Drift detection in the interconnected CSTRs 152 4.1.11
Information fusion from UKF 152 4.1.12 Drift detection in the four-tank
system 156 4.2 Observer-Based Active Structures 160 4.2.1 Problem statement
160 4.2.2 A separation principle 162 4.2.3 FDI residuals 164 4.2.4 Control
of integrity 164 4.2.5 Overall stability 165 4.2.6 Design outline 165 4.2.7
Design of an active FTC scheme 166 4.2.8 Extraction of FDI-FTC pairs 166
4.2.9 Simulation 169 4.3 Notes 172 References 172 5 Fault-Tolerant
Nonlinear Control Systems 175 5.1 Comparison of Fault Detection Schemes 175
5.2 Fault Detection in Nonlinear Systems 176 5.3 Nonlinear Observer-Based
Residual Generation Schemes 176 5.3.1 General considerations 176 5.3.2
Extended Luenberger observer 177 5.3.3 Nonlinear identity observer approach
177 5.3.4 Unknown input observer approach 178 5.3.5 The disturbance
decoupling nonlinear observer approach 178 5.3.6 Adaptive nonlinear
observer approach 178 5.3.7 High-gain observer approach 178 5.3.8
Sliding-mode observer approach 178 5.3.9 Geometric approach 179 5.3.10
Game-theoretic approach 179 5.3.11 Observers for Lipschitz nonlinear
systems 179 5.3.12 Lyapunov-reconstruction-based passive scheme 180 5.3.13
Time-varying results 185 5.3.14 Optimization-based active scheme 187 5.3.15
Learning-based active scheme 190 5.3.16 Adaptive backstepping-based active
scheme 191 5.3.17 Switched control-based active scheme 193 5.3.18
Predictive control-based active scheme 195 5.4 Integrated Control
Reconfiguration Scheme 197 5.4.1 Introduction 197 5.4.2 Basic features 198
5.4.3 Nonlinear model of a pendulum on a cart 199 5.4.4 NGA adaptive filter
design 201 5.4.5 Simulation results 207 5.4.6 Performance evaluation 209
5.4.7 Comparative studies 211 5.5 Notes 215 References 215 6 Robust Fault
Estimation 219 6.1 Introduction 219 6.2 System Description 220 6.3
Multiconstrained Fault Estimation 221 6.3.1 Observer design 221 6.3.2
Existence conditions 226 6.3.3 Improved results 228 6.3.4 Simulation
results 232 6.4 Adaptive Fault Estimation 235 6.4.1 Introduction 236 6.4.2
Problem statement 238 6.4.3 Robust adaptive estimation 239 6.4.4 Internal
stability analysis 240 6.4.5 Robust performance index 241 6.4.6 Simulation
242 6.5 Adaptive Tracking Control Scheme 244 6.5.1 Attitude dynamics 244
6.5.2 Fault detection scheme 248 6.5.3 Fault-tolerant tracking scheme 250
6.6 Notes 254 References 254 7 Fault Detection of Networked Control Systems
257 7.1 Introduction 257 7.2 Problem Formulation 258 7.3 Modified Residual
Generator Scheme 259 7.3.1 Modified residual generator and dynamic analysis
259 7.3.2 Residual evaluation 261 7.3.3 Co-design of residual generator and
evaluation 264 7.4 Quantized Fault-Tolerant Control 267 7.4.1 Introduction
267 7.4.2 Problem statement 268 7.4.3 Quantized control design 271 7.4.4
Simulation 276 7.5 Sliding-Mode Observer 278 7.5.1 Introduction 278 7.5.2
Dynamic model 280 7.5.3 Limited state measurements 286 7.5.4 Simulation
results: full state measurements 290 7.5.5 Simulation results: partial
state measurements 293 7.6 Control of Linear Switched Systems 294 7.6.1
Introduction 295 7.6.2 Problem formulation 295 7.6.3 Stability of a
closed-loop system 296 7.6.4 Simulation 300 7.7 Notes 303 References 303 8
Industrial Fault-Tolerant Architectures 307 8.1 Introduction 307 8.2 System
Architecture 308 8.3 Architecture of a Fault-Tolerant Node 309 8.3.1 Basic
architecture 309 8.3.2 Architecture with improved reliability 310 8.3.3
Symmetric node architecture 310 8.3.4 Results 311 8.4 Recovery Points 312
8.5 Networks 314 8.6 System Fault Injection and Monitoring 315 8.6.1
Monitoring systems 315 8.6.2 Design methodology 316 8.7 Notes 318
References 319 9 Fault Estimation for Stochastic Systems 321 9.1
Introduction 321 9.2 Actuator Fault Diagnosis Design 322 9.3 Fault-Tolerant
Controller Design 324 9.4 Extension to an Unknown Input Case 325 9.5
Aircraft Application 326 9.5.1 Transforming the system into standard form
327 9.5.2 Simulation results 329 9.6 Router Fault Accommodation in Real
Time 330 9.6.1 Canonical controller and achievable behavior 333 9.6.2
Router modeling and desired behavior 334 9.6.3 Description of fault
behavior 336 9.6.4 A least restrictive controller 338 9.7 Fault Detection
for Markov Jump Systems 338 9.7.1 Introduction 339 9.7.2 Problem
formulation 340 9.7.3 H infinity bounded real lemmas 343 9.7.4 H infinity
FD filter design 345 9.7.5 Simulation 347 9.8 Notes 352 References 353 10
Applications 355 10.1 Detection of Abrupt Changes in an Electrocardiogram
355 10.1.1 Introduction 355 10.1.2 Modeling ECG signals with an AR model
356 10.1.3 Linear models with additive abrupt changes 358 10.1.4 Off-line
detection of abrupt changes in ECG 361 10.1.5 Online detection of abrupt
changes in ECG 363 10.2 Detection of Abrupt Changes in the Frequency Domain
365 10.2.1 Introduction 365 10.2.2 Problem formulation 366 10.2.3 Frequency
domain ML ratio estimation 368 10.2.4 Likelihood of the hypothesis of no
abrupt change 372 10.2.5 Effect of an abrupt change 374 10.2.6 Simulation
results 382 10.3 Electromechanical Positioning System 383 10.3.1
Introduction 383 10.3.2 Problem formulation 385 10.3.3 Test bed 386 10.4
Application to Fermentation Processes 387 10.4.1 Nonlinear faulty dynamic
system 388 10.4.2 Residual characteristics 389 10.4.3 The parameter filter
399 10.4.4 Fault filter 400 10.4.5 Fault isolation and identification 401
10.4.6 Isolation speed 401 10.4.7 Parameter partition 402 10.4.8 Adaptive
intervals 402 10.4.9 Simulation studies 405 10.5 Flexible-Joint Robots 415
10.5.1 Problem formulation 415 10.5.2 Fault detection scheme 417 10.5.3
Adaptive fault accommodation control 420 10.5.4 Control with prescribed
performance bounds 422 10.5.5 Simulation results 425 10.6 Notes 429
References 430 A Supplementary Information 435 A.1 Notation 435 A.1.1
Kronecker products 436 A.1.2 Some definitions 437 A.1.3 Matrix lemmas 438
A.2 Results from Probability Theory 440 A.2.1 Results-A 440 A.2.2 Results-B
441 A.2.3 Results-C 441 A.2.4 Minimum mean square estimate 442 A.3
Stability Notions 444 A.3.1 Practical stabilizability 444 A.3.2 Razumikhin
stability 445 A.4 Basic Inequalities 447 A.4.1 Schur complements 447 A.4.2
Bounding inequalities 449 A.5 Linear Matrix Inequalities 453 A.5.1 Basics
453 A.5.2 Some standard problems 454 A.5.3 The S-procedure 455 A.6 Some
Formulas on Matrix Inverses 456 A.6.1 Inverse of block matrices 456 A.6.2
Matrix inversion lemma 457 References 458 Index 459
Concepts of Faults 2 1.3 Classification of Fault Detection Methods 3 1.3.1
Hardware redundancy based fault detection 3 1.3.2 Plausibility test 3 1.3.3
Signal-based fault diagnosis 4 1.3.4 Model-based fault detection 5 1.4
Types of Fault-Tolerant Control System 8 1.5 Objectives and Structure of
AFTCS 8 1.6 Classification of Reconfigurable Control Methods 10 1.6.1
Classification based on control algorithms 10 1.6.2 Classification based on
field of application 11 1.7 Outline of the Book 11 1.7.1 Methodology 11
1.7.2 Chapter organization 12 1.8 Notes 13 References 13 2 Fault Diagnosis
and Detection 17 2.1 Introduction 17 2.2 Related Work 17 2.2.1 Model-based
schemes 17 2.2.2 Model-free schemes 18 2.2.3 Probabilistic schemes 19 2.3
Integrated Approach 19 2.3.1 Improved multi-sensor data fusion 19 2.3.2
Unscented transformation 21 2.3.3 Unscented Kalman filter 22 2.3.4
Parameter estimation 23 2.3.5 Multi-sensor integration architectures 24 2.4
Robust Unscented Kalman Filter 26 2.4.1 Introduction 26 2.4.2 Problem
formulation 28 2.4.3 Residual generation 29 2.4.4 Residual evaluation 29
2.5 Quadruple Tank System 30 2.5.1 Model of the QTS 31 2.5.2 Fault
scenarios in QTS 32 2.5.3 Implementation structure of UKF 33 2.5.4 UKF with
centralized multi-sensor data fusion 35 2.5.5 UKF with decentralized
multi-sensor data fusion 35 2.5.6 Drift detection 35 2.6 Industrial Utility
Boiler 38 2.6.1 Steam flow dynamics 38 2.6.2 Drum pressure dynamics 40
2.6.3 Drum level dynamics 40 2.6.4 Steam temperature 41 2.6.5 Fault model
for the utility boiler 42 2.6.6 Fault scenarios in the utility boiler 43
2.6.7 UKF with centralized multi-sensor data fusion 43 2.6.8 UKF with
decentralized multi-sensor data fusion 43 2.6.9 Drift detection 45 2.6.10
Remarks 45 2.7 Notes 46 References 46 3 Robust Fault Detection 49 3.1
Distributed Fault Diagnosis 49 3.1.1 Introduction 49 3.1.2 System model 50
3.1.3 Distributed FDI architecture 55 3.1.4 Distributed fault detection
method 55 3.1.5 Adaptive thresholds 57 3.1.6 Distributed fault isolation
method 62 3.1.7 Adaptive thresholds for DFDI 64 3.1.8 Fault detectability
condition 67 3.1.9 Fault isolability analysis 69 3.1.10 Stability and
learning capability 71 3.2 Robust Fault Detection Filters 74 3.2.1
Reference model 74 3.2.2 Design of adaptive threshold 76 3.2.3 Iterative
update of noise mean and covariance 77 3.2.4 Unscented transformation (UT)
79 3.2.5 Car-like mobile robot application 82 3.3 Simultaneous Fault
Detection and Control 90 3.3.1 Introduction 93 3.3.2 System model 93 3.3.3
Problem formulation 95 3.3.4 Simultaneous fault detection and control
problem 96 3.3.5 Two-tank system simulation 106 3.4 Data-Driven Fault
Detection Design 108 3.4.1 Introduction 109 3.4.2 Problem formulation 111
3.4.3 Selection of weighting matrix 112 3.4.4 Design of FDF for time-delay
system 113 3.4.5 LMI design approach 114 3.4.6 Four-tank system simulation
119 3.5 Robust Adaptive Fault Estimation 122 3.5.1 Introduction 124 3.5.2
Problem statement 125 3.5.3 Adaptive observer 127 3.6 Notes 131 References
131 4 Fault-Tolerant Control Systems 135 4.1 Model Prediction-Based Design
Approach 135 4.1.1 Introduction 135 4.1.2 System description 136 4.1.3
Discrete-time UKF 138 4.1.4 Unscented Transformation (UT) 141 4.1.5
Controller reconfiguration 143 4.1.6 Model predictive control 144 4.1.7
Interconnected CSTR units 149 4.1.8 Four-tank system 151 4.1.9 Simulation
results 152 4.1.10 Drift detection in the interconnected CSTRs 152 4.1.11
Information fusion from UKF 152 4.1.12 Drift detection in the four-tank
system 156 4.2 Observer-Based Active Structures 160 4.2.1 Problem statement
160 4.2.2 A separation principle 162 4.2.3 FDI residuals 164 4.2.4 Control
of integrity 164 4.2.5 Overall stability 165 4.2.6 Design outline 165 4.2.7
Design of an active FTC scheme 166 4.2.8 Extraction of FDI-FTC pairs 166
4.2.9 Simulation 169 4.3 Notes 172 References 172 5 Fault-Tolerant
Nonlinear Control Systems 175 5.1 Comparison of Fault Detection Schemes 175
5.2 Fault Detection in Nonlinear Systems 176 5.3 Nonlinear Observer-Based
Residual Generation Schemes 176 5.3.1 General considerations 176 5.3.2
Extended Luenberger observer 177 5.3.3 Nonlinear identity observer approach
177 5.3.4 Unknown input observer approach 178 5.3.5 The disturbance
decoupling nonlinear observer approach 178 5.3.6 Adaptive nonlinear
observer approach 178 5.3.7 High-gain observer approach 178 5.3.8
Sliding-mode observer approach 178 5.3.9 Geometric approach 179 5.3.10
Game-theoretic approach 179 5.3.11 Observers for Lipschitz nonlinear
systems 179 5.3.12 Lyapunov-reconstruction-based passive scheme 180 5.3.13
Time-varying results 185 5.3.14 Optimization-based active scheme 187 5.3.15
Learning-based active scheme 190 5.3.16 Adaptive backstepping-based active
scheme 191 5.3.17 Switched control-based active scheme 193 5.3.18
Predictive control-based active scheme 195 5.4 Integrated Control
Reconfiguration Scheme 197 5.4.1 Introduction 197 5.4.2 Basic features 198
5.4.3 Nonlinear model of a pendulum on a cart 199 5.4.4 NGA adaptive filter
design 201 5.4.5 Simulation results 207 5.4.6 Performance evaluation 209
5.4.7 Comparative studies 211 5.5 Notes 215 References 215 6 Robust Fault
Estimation 219 6.1 Introduction 219 6.2 System Description 220 6.3
Multiconstrained Fault Estimation 221 6.3.1 Observer design 221 6.3.2
Existence conditions 226 6.3.3 Improved results 228 6.3.4 Simulation
results 232 6.4 Adaptive Fault Estimation 235 6.4.1 Introduction 236 6.4.2
Problem statement 238 6.4.3 Robust adaptive estimation 239 6.4.4 Internal
stability analysis 240 6.4.5 Robust performance index 241 6.4.6 Simulation
242 6.5 Adaptive Tracking Control Scheme 244 6.5.1 Attitude dynamics 244
6.5.2 Fault detection scheme 248 6.5.3 Fault-tolerant tracking scheme 250
6.6 Notes 254 References 254 7 Fault Detection of Networked Control Systems
257 7.1 Introduction 257 7.2 Problem Formulation 258 7.3 Modified Residual
Generator Scheme 259 7.3.1 Modified residual generator and dynamic analysis
259 7.3.2 Residual evaluation 261 7.3.3 Co-design of residual generator and
evaluation 264 7.4 Quantized Fault-Tolerant Control 267 7.4.1 Introduction
267 7.4.2 Problem statement 268 7.4.3 Quantized control design 271 7.4.4
Simulation 276 7.5 Sliding-Mode Observer 278 7.5.1 Introduction 278 7.5.2
Dynamic model 280 7.5.3 Limited state measurements 286 7.5.4 Simulation
results: full state measurements 290 7.5.5 Simulation results: partial
state measurements 293 7.6 Control of Linear Switched Systems 294 7.6.1
Introduction 295 7.6.2 Problem formulation 295 7.6.3 Stability of a
closed-loop system 296 7.6.4 Simulation 300 7.7 Notes 303 References 303 8
Industrial Fault-Tolerant Architectures 307 8.1 Introduction 307 8.2 System
Architecture 308 8.3 Architecture of a Fault-Tolerant Node 309 8.3.1 Basic
architecture 309 8.3.2 Architecture with improved reliability 310 8.3.3
Symmetric node architecture 310 8.3.4 Results 311 8.4 Recovery Points 312
8.5 Networks 314 8.6 System Fault Injection and Monitoring 315 8.6.1
Monitoring systems 315 8.6.2 Design methodology 316 8.7 Notes 318
References 319 9 Fault Estimation for Stochastic Systems 321 9.1
Introduction 321 9.2 Actuator Fault Diagnosis Design 322 9.3 Fault-Tolerant
Controller Design 324 9.4 Extension to an Unknown Input Case 325 9.5
Aircraft Application 326 9.5.1 Transforming the system into standard form
327 9.5.2 Simulation results 329 9.6 Router Fault Accommodation in Real
Time 330 9.6.1 Canonical controller and achievable behavior 333 9.6.2
Router modeling and desired behavior 334 9.6.3 Description of fault
behavior 336 9.6.4 A least restrictive controller 338 9.7 Fault Detection
for Markov Jump Systems 338 9.7.1 Introduction 339 9.7.2 Problem
formulation 340 9.7.3 H infinity bounded real lemmas 343 9.7.4 H infinity
FD filter design 345 9.7.5 Simulation 347 9.8 Notes 352 References 353 10
Applications 355 10.1 Detection of Abrupt Changes in an Electrocardiogram
355 10.1.1 Introduction 355 10.1.2 Modeling ECG signals with an AR model
356 10.1.3 Linear models with additive abrupt changes 358 10.1.4 Off-line
detection of abrupt changes in ECG 361 10.1.5 Online detection of abrupt
changes in ECG 363 10.2 Detection of Abrupt Changes in the Frequency Domain
365 10.2.1 Introduction 365 10.2.2 Problem formulation 366 10.2.3 Frequency
domain ML ratio estimation 368 10.2.4 Likelihood of the hypothesis of no
abrupt change 372 10.2.5 Effect of an abrupt change 374 10.2.6 Simulation
results 382 10.3 Electromechanical Positioning System 383 10.3.1
Introduction 383 10.3.2 Problem formulation 385 10.3.3 Test bed 386 10.4
Application to Fermentation Processes 387 10.4.1 Nonlinear faulty dynamic
system 388 10.4.2 Residual characteristics 389 10.4.3 The parameter filter
399 10.4.4 Fault filter 400 10.4.5 Fault isolation and identification 401
10.4.6 Isolation speed 401 10.4.7 Parameter partition 402 10.4.8 Adaptive
intervals 402 10.4.9 Simulation studies 405 10.5 Flexible-Joint Robots 415
10.5.1 Problem formulation 415 10.5.2 Fault detection scheme 417 10.5.3
Adaptive fault accommodation control 420 10.5.4 Control with prescribed
performance bounds 422 10.5.5 Simulation results 425 10.6 Notes 429
References 430 A Supplementary Information 435 A.1 Notation 435 A.1.1
Kronecker products 436 A.1.2 Some definitions 437 A.1.3 Matrix lemmas 438
A.2 Results from Probability Theory 440 A.2.1 Results-A 440 A.2.2 Results-B
441 A.2.3 Results-C 441 A.2.4 Minimum mean square estimate 442 A.3
Stability Notions 444 A.3.1 Practical stabilizability 444 A.3.2 Razumikhin
stability 445 A.4 Basic Inequalities 447 A.4.1 Schur complements 447 A.4.2
Bounding inequalities 449 A.5 Linear Matrix Inequalities 453 A.5.1 Basics
453 A.5.2 Some standard problems 454 A.5.3 The S-procedure 455 A.6 Some
Formulas on Matrix Inverses 456 A.6.1 Inverse of block matrices 456 A.6.2
Matrix inversion lemma 457 References 458 Index 459