Читать книгу Diagnosis and Fault-tolerant Control Volume 2 - Группа авторов - Страница 2
ОглавлениеTable 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
List of Illustrations
1 Chapter 1Figure 1.1. Fault diagnosis moduleFigure 1.2. Model-based fault diagnosis strategyFigure 1.3. Residual generation strategyFigure 1.4. Residual generator input–output formFigure 1.5. Parity vector approachFigure 1.6. MIMO parity vectorFigure 1.7. Diagnostic residual observerFigure 1.8. Data-driven fault diagnosisFigure 1.9. Neuron representation exampleFigure 1.10. Nonlinear ARX neural networkFigure 1.11. Example of dynamic neural networkFigure 1.12. Fault diagnosis approach integrationFigure 1.13. Online estimation for fault diagnosis
2 Chapter 2Figure 2.1. Anaerobic bioreactorFigure 2.2. The referential inputsFigure 2.3. The system output y1 (t): nominal output, output without FTC and out...Figure 2.4. The system output y2 (t): nominal output, output without FTC and out...Figure 2.5. The real actuator fault and its estimatedFigure 2.6. The real actuator fault and its estimated
3 Chapter 3Figure 3.1. The structure of the developed RNNFigure 3.2. Robust predictive FT schemeFigure 3.3. Comparison between nonlinear and Takagi–Sugeno response of the syste...Figure 3.4. Actuator faults f a,1 (a) and fa,2 (b). For a color version of this ...Figure 3.5. Sensor faults f s,1 (a) and fs,2 (b). For a color version of this fi...Figure 3.6. State variables ωv (a) and ωh (b). For a color version of this figur...Figure 3.7. State variables ωv (a) and ωh (b). For a color version of this figur...Figure 3.8. Main and tail rotor angular position based on FTC and non-FTC (a) as...Figure 3.9. Appropriate control inputs u1 and u2. For a color version of this fi...
4 Chapter 4Figure 4.1. Idea of MPC. For a color version of this figure, see www.iste.co.uk/...Figure 4.2. Neural network with external dynamicsFigure 4.3. Scheme of pneumatic servomechanismFigure 4.4. Modeling: process output (blue-solid) and model output (red-dashed)....Figure 4.5. Uncertainty modeling: process output (black-solid), fundamental mode...Figure 4.6. Control performance for random steps: reference profile (black-dashe...Figure 4.7. Control performance for the ramp signal: reference profile (black-da...Figure 4.8. Control performance for harmonic steps: reference profile (black-das...
5 Chapter 6Figure 6.1. The quadruple-tank systemFigure 6.2. Virtual actuator results. For a color version of this figure, see ww...Figure 6.3. Virtual sensor results. For a color version of this figure, see www....
6 Chapter 8Figure 8.1. Key points of sustainable approachFigure 8.2. Link between objectives, overall goals and impactsFigure 8.3. Sustainable control design tasksFigure 8.4. Sustainable design strategy tasks and targets
List of Tables
1 Chapter 1Table 1.1. Training algorithm examples
2 Chapter 2Table 2.1. Constant parameter values
3 Chapter 4Table 4.1. Control results for RNMPCTable 4.2. Control quality
4 Chapter 6Table 6.1. Model parameters
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