Table of Contents
1 Cover
4 1 Nonlinear Methods for Fault Diagnosis 1.1. Introduction 1.2. Fault diagnosis tasks 1.3. Model-based fault diagnosis 1.4. Data-driven fault diagnosis 1.5. Model-based and data-driven integrated fault diagnosis 1.6. Robust fault diagnosis problem 1.7. Summary 1.8. References
5 2 Linear Parameter Varying Methods 2.1. Introduction 2.2. Preliminaries: a classical approach 2.3. Problem statement 2.4. Robust active fault-tolerant control design 2.5. Application: an anaerobic bioreactor 2.6. Conclusion 2.7. References
6 3 Fuzzy and Neural Network Approaches 3.1. Introduction 3.2. Fuzzy model design 3.3. Neural model design 3.4. Fault estimation and diagnosis 3.5. Fault-tolerant control 3.6. Illustrative examples 3.7. Conclusion 3.8. Acknowledgment 3.9. References
7 4 Model Predictive Control Methods 4.1. Introduction 4.2. Idea of MPC 4.3. Robustness of MPC 4.4. Neural-network-based robust MPC 4.5. Robust control of a pneumatic servo 4.6. Conclusion 4.7. References
8 5 Nonlinear Modeling for Fault-tolerant Control 5.1. Introduction 5.2. Fault-tolerant control strategies 5.3. Fault diagnosis and tolerant control 5.4. Summary 5.5. References
9 6 Virtual Sensors and Actuators 6.1. Introduction 6.2. Problem statement 6.3. Virtual sensors and virtual actuators 6.4. LMI-based design 6.5. Additional considerations 6.6. Application example 6.7. Conclusion 6.8. References
10 7 Conclusions 7.1. Introduction 7.2. Closing remarks 7.3. References
11 8 Open Research Issues 8.1. Further works and open problems 8.2. Summary 8.3. References
13 Index
Guide
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