Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/101077
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Type: Journal article
Title: Novel neural networks-based fault tolerant control scheme with fault alarm
Author: Shen, Q.
Jiang, B.
Shi, P.
Lim, C.
Citation: IEEE Transactions on Cybernetics, 2014; 44(11):2190-2201
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2014
ISSN: 2168-2267
2168-2275
Statement of
Responsibility: 
Qikun Shen, Bin Jiang, Peng Shi, and Cheng-Chew Lim
Abstract: In this paper, the problem of adaptive active faulttolerant control for a class of nonlinear systems with unknown actuator fault is investigated. The actuator fault is assumed to have no traditional affine appearance of the system state variables and control input. The useful property of the basis function of the radial basis function neural network (NN), which will be used in the design of the fault tolerant controller, is explored. Based on the analysis of the design of normal and passive fault tolerant controllers, by using the implicit function theorem, a novel NN-based active fault-tolerant control scheme with fault alarm is proposed. Comparing with results in the literature, the fault-tolerant control scheme can minimize the time delay between fault occurrence and accommodation that is called the time delay due to fault diagnosis, and reduce the adverse effect on system performance. In addition, the FTC scheme has the advantages of a passive faulttolerant control scheme as well as the traditional active faulttolerant control scheme’s properties. Furthermore, the faulttolerant control scheme requires no additional fault detection and isolation model which is necessary in the traditional active faulttolerant control scheme. Finally, simulation results are presented to demonstrate the efficiency of the developed techniques.
Keywords: Adaptive control; FTC; neural networks (NNs)-based control
Rights: © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
RMID: 0030014585
DOI: 10.1109/TCYB.2014.2303131
Grant ID: http://purl.org/au-research/grants/arc/DP140102180
Appears in Collections:Electrical and Electronic Engineering publications

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