Intelligent fault-tolerant control using adaptive and learning methods

Stimulated by the growing demand for improving system performance and reliability, fault-tolerant system design has been receiving significant attention. This paper proposes a new fault-tolerant control methodology using adaptive estimation and control approaches based on the learning capabilities of neural networks or fuzzy systems. On-line approximation-based stable adaptive neural/fuzzy control is studied for a class of input-output feedback linearizable time-varying nonlinear systems. This class of systems is large enough so that it is not only of theoretical interest but also of practical applicability. Moreover, the fault-tolerance ability of the adaptive controller has been further improved by exploiting information estimated from a fault-diagnosis unit designed by interfacing multiple models with an expert supervisory scheme. Simulation examples for a fault-tolerant jet engine control problem are given to demonstrate the effectiveness of the proposed scheme. © 2002 Published by Elsevier Science Ltd.