Fault diagnosis and fault-tolerant control strategies for non-linear systems [electronic resource] : analytical and soft computing approaches /
Witczak, Marcin.author.
  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.
Description based upon print version of record.
Preface; Contents; Symbols; Abbreviations; 1 Introduction; 1.1 Introductory Background; 1.2 Content; References; Part IRobust Fault Diagnosis; 2 Unknown Input Observers and Filters; 2.1 Unknown Input Decoupling; 2.2 Preventing Fault Decoupling; 2.3 First- and Second-order Extended Unknown  Input Observers; 2.3.1 Convergence Analysis; 2.3.2 Design Principles; 2.4 Unscented Kalman Filter; 2.4.1 Unscented Transform; 2.4.2 Principle of the UKF-based UIF; 2.5 Determination of an Unknown Input  Distribution Matrix; 2.6 Design of the UIF with Varying Unknown Input  Distribution Matrices
2.7 Illustrative Examples2.7.1 Estimation of E for an Induction Motor; 2.7.2 Varying E Case; 2.7.3 Fault Detection and Isolation of a Two-Tank System; 2.7.4 First- Versus Second-order EUIO; 2.8 Concluding Remarks; References; 3 Neural Network-based Approaches  to Fault Diagnosis; 3.1 Robust Fault Detection with the Multi-Layer Perceptron; 3.1.1 Illustrative Example; 3.1.2 Algorithms and Properties of D-OED for Neural Networks; 3.1.3 Industrial Application; 3.2 GMDH Neural Networks; 3.2.1 Model Uncertainty in the GMDH Neural Network; 3.2.2 Bounded-Error Approach
3.2.3 Synthesis of the GMDH Neural Network Via the BEA3.2.4 Robust Fault Detection with the GMDH Model; 3.2.5 Alternative Robust Fault Detection Procedure:  A Backward Detection Test; 3.2.6 Industrial Application; 3.3 Concluding Remarks; References; Part IIIntegrated Fault Diagnosis and Control; 4 Integrated Fault Diagnosis and Control: Principles and Design Strategies; 4.1 FTC Strategy; 4.1.1 Fault Identification; 4.1.2 Stabilisation Problem; 4.1.3 Observer Design; 4.1.4 Integrated Design Procedure; 4.2 Extension to Non-linear Systems; 4.3 Constrained State Estimation
4.3.1 Complete Design Procedure4.4 Application Example; 4.4.1 Description of the Twin-Rotor MIMO System; 4.4.2 Non-linear Reference Model of Twin-Rotor MIMO System; 4.4.3 Fault Scenario 1; 4.4.4 Fault Scenario 2; 4.4.5 Fault Scenario 3; 4.5 Concluding Remarks; References; 5 Robust H-Based Approaches; 5.1 Towards Robust Fault-Tolerant Control; 5.1.1 Preliminaries; 5.1.2 Fault Estimation Approach; 5.1.3 Integrated FTC Design; 5.1.4 Illustrative Example: Fault Estimation; 5.2 Complete Robust Design of Fault-Tolerant Control; 5.2.1 Preliminaries; 5.2.2 Fault Estimation Strategy
5.2.3 Guaranteed Decay Rate5.2.4 Integrated FTC Design; 5.2.5 Illustrative Example: Fault Estimation; 5.2.6 Illustrative Examples: Fault-Tolerant Control; 5.3 Robust Design for Quasi-LPV Systems; 5.3.1 Actuator Fault Estimation; 5.3.2 Sensor Fault Estimation; 5.3.3 Integrated FTC Design; 5.3.4 Illustrative Example: Fault Estimation of a Multi-Tank System; 5.4 Concluding Remarks; References; 6 Fuzzy Multiple-Model Approach  to Fault-Tolerant Control; 6.1 Essentials of Fuzzy Logic; 6.2 Fuzzy Multiple-Model Representation; 6.3 Development of the Takagi--Sugeno Fuzzy Model
6.4 Virtual Fuzzy Actuators
Includes bibliographical references and index.
  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.
Control engineering.
Computational intelligence.
Artificial intelligence.
Computational complexity.
Control and Systems Theory.
Computational Intelligence.
Artificial Intelligence.
Complexity.

Lecture Notes in Electrical Engineering