Robust and Fault-Tolerant Control: Neural-Network-Based Solutions

Description

Robust and Fault-Tolerant Control proposes novel automatic control strategies for nonlinear systems developed by means of artificial neural networks and pays special attention to robust and fault-tolerant approaches. The book discusses robustness and fault tolerance in the context of model predictive control, fault accommodation and reconfiguration, and iterative learning control strategies. Expanding on its theoretical deliberations the monograph includes many case studies demonstrating how the proposed approaches work in practice. The most important features of the book include:a comprehensive review of neural network architectures with possible applications in system modelling and control; a concise introduction to robust and fault-tolerant control; step-by-step presentation of the control approaches proposed; an abundance of case studies illustrating the important steps in designing robust and fault-tolerant control; and a large number of figures and tables facilitating the performance analysis of the control approaches described. The material presented in this book will be useful for researchers and engineers who wish to avoid spending excessive time in searching neural-network-based control solutions. It is written for electrical, computer science and automatic control engineers interested in control theory and their applications. This monograph will also interest postgraduate students engaged in self-study of nonlinear robust and fault-tolerant control. Front Matter ....Pages i-xxviii Introduction (Krzysztof Patan)....Pages 1-7 Neural Networks (Krzysztof Patan)....Pages 9-58 Robust and Fault-Tolerant Control (Krzysztof Patan)....Pages 59-76 Model Predictive Control (Krzysztof Patan)....Pages 77-129 Control Reconfiguration (Krzysztof Patan)....Pages 131-167 Iterative Learning Control (Krzysztof Patan)....Pages 169-201 Concluding Remarks and Further Research Directions (Krzysztof Patan)....Pages 203-205 Back Matter ....Pages 207-209