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

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.