Shashanka Ubaru, Lior Horesh, et al.
Journal of Biomedical Informatics
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.
Shashanka Ubaru, Lior Horesh, et al.
Journal of Biomedical Informatics
Satoshi Hada
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Karthik Visweswariah, Sanjeev Kulkarni, et al.
IEEE International Symposium on Information Theory - Proceedings
F.M. Schellenberg, M. Levenson, et al.
BACUS Symposium on Photomask Technology and Management 1991