Diagnosis and Fault-tolerant Control Volume 2

From Fault Diagnosis to Fault-tolerant Control

Inbunden, Engelska, 2022

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Beskrivning

This book presents recent advances in fault diagnosis and fault-tolerant control of dynamic processes. Its impetus derives from the need for an overview of the challenges of the fault diagnosis technique and sustainable control, especially for those demanding systems that require reliability, availability, maintainability, and safety to ensure efficient operations. Moreover, the need for a high degree of tolerance with respect to possible faults represents a further key point, primarily for complex systems, as modeling and control are inherently challenging, and maintenance is both expensive and safety-critical.Diagnosis and Fault-tolerant Control 2 also presents and compares different fault diagnosis and fault-tolerant schemes, using well established, innovative strategies for modeling the behavior of the dynamic process under investigation. An updated treatise of diagnosis and fault-tolerant control is addressed with the use of essential and advanced methods including signal-based, model-based and data-driven techniques. Another key feature is the application of these methods for dealing with robustness and reliability.

Produktinformation

Utgivningsdatum:2022-01-04
Mått:10 x 10 x 10 mm
Vikt:454 g
Format:Inbunden
Språk:Engelska
Antal sidor:288
Förlag:ISTE Ltd
ISBN:9781789450590

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Mer om författaren

Vicenc Puig is Professor of Automatic Control at the Universitat Politècnica de Catalunya (UPC), Spain. He has published more than 80 journal articles and more than 350 articles in international conference/workshop proceedings related to diagnosis and faulttolerant control.Silvio Simani is Professor of Automatic Control in the Engineering Department of Ferrara University, Italy. He has published about 260 journal and conference papers, several book chapters and four monographs on fault diagnosis and sustainable control topics.

Innehållsförteckning

Chapter 1 Nonlinear Methods for Fault Diagnosis 1Silvio Simani and Paolo Castaldi1.1. Introduction 11.2. Fault diagnosis tasks 21.2.1. Residual generation task 51.2.2. Residual evaluation task 81.3. Model-based fault diagnosis 91.3.1. Parity space relations 91.3.2. Observer-based approaches 121.3.3. Nonlinear filtering methods 141.3.4. Nonlinear geometric approach strategy 171.4. Data-driven fault diagnosis 201.4.1. Online identification methods 211.4.2. Machine learning approaches to fault diagnosis 241.5. Model-based and data-driven integrated fault diagnosis 341.6. Robust fault diagnosis problem 421.7. Summary 471.8. References 48Chapter 2 Linear Parameter Varying Methods 57Mickael Rodrigues, Habib Hamdi and Didier Theilliol2.1. Introduction 572.2. Preliminaries: a classical approach 602.3. Problem statement 622.4. Robust active fault-tolerant control design 652.4.1. Robust observer-based FTC design 652.4.2. Stability analysis 682.5. Application: an anaerobic bioreactor 752.6. Conclusion 812.7. References 81Chapter 3 Fuzzy and Neural Network Approaches 85Marcin Witczak, Marcin Pazera, Norbert Kukurowski and Marcin Mrugalski3.1. Introduction 853.2. Fuzzy model design 873.2.1. Takagi–Sugeno systems 873.2.2. Generation of TS models via nonlinear embedding 883.3. Neural model design 903.3.1. Recurrent neural network 903.3.2. Identification of the neural model uncertainty 933.4. Fault estimation and diagnosis 943.4.1. Actuator fault estimation using neural networks 943.4.2. Sensor and actuator fault estimation using fuzzy logic 973.5. Fault-tolerant control 1013.5.1. An overview of the fault-tolerant scheme 1013.5.2. Robust fault estimation and control 1033.5.3. Derivation of a robust invariant set 1063.5.4. Efficient predictive FTC 1063.6. Illustrative examples 1103.6.1. Sensor and actuator fault estimation example 1103.6.2. Fault-tolerant control example 1133.7. Conclusion 1153.8. Acknowledgment 1163.9. References 116Chapter 4 Model Predictive Control Methods 121Krzysztof Patan4.1. Introduction 1214.2. Idea of MPC 1224.3. Robustness of MPC 1254.4. Neural-network-based robust MPC 1264.4.1. Neural network models 1274.4.2. Nonlinear MPC 1304.4.3. Approximate MPC 1304.4.4. Robust nonlinear MPC 1324.4.5. Robust approximate MPC 1324.5. Robust control of a pneumatic servo 1344.5.1. Robust nonlinear neural-network-based MPC 1354.5.2. Robust approximate neural-network-based MPC 1394.6. Conclusion 1404.7. References 140Chapter 5 Nonlinear Modeling for Fault-tolerant Control 143Silvio Simani and Paolo Castaldi5.1. Introduction 1435.1.1. Joint fault diagnosis and control 1475.1.2. Nonlinear adaptive fault estimators 1495.1.3. Fuzzy fault-tolerant control 1615.1.4. Recursive adaptive control 1645.1.5. Sustainable control 1745.2. Fault-tolerant control strategies 1755.2.1. Fault tolerance and compensation 1775.3. Fault diagnosis and tolerant control 1805.3.1. Fault-tolerant control design 1835.4. Summary 1865.5. References 187Chapter 6 Virtual Sensors and Actuators 193Damiano Rotondo and Vicenç Puig6.1. Introduction 1936.2. Problem statement 1946.3. Virtual sensors and virtual actuators 1986.4. LMI-based design 2026.5. Additional considerations 2056.6. Application example 2086.6.1. Virtual actuator 2096.6.2. Virtual sensors 2106.7. Conclusion 2126.8. References 212Chapter 7 Conclusions 215Vicenç Puig and Silvio Simani7.1. Introduction 2157.2. Closing remarks 2197.3. References 229Chapter 8 Open Research Issues 241Vicenç Puig and Silvio Simani8.1. Further works and open problems 2418.1.1. Sustainable control design objectives 2438.1.2. Sustainable control concepts and approaches 2478.1.3. Sustainable control approaches and working methods 2498.1.4. Sustainable control design ambition 2538.1.5. Sustainable control innovation potentials 2588.1.6. Sustainable control expected impacts 2598.2. Summary 2618.3. References 262List of Authors 265Index 267Summary of Volume 1 271