Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/99214
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Type: Journal article
Title: Adaptive neural fault-tolerant control of a 3-DOF model helicopter system
Author: Chen, M.
Shi, P.
Lim, C.
Citation: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016; 46(2):260-270
Publisher: IEEE
Issue Date: 2016
ISSN: 2168-2216
2168-2232
Statement of
Responsibility: 
Mou Chen, Peng Shi and Cheng-Chew Lim
Abstract: In this paper, an adaptive neural fault-tolerant control scheme is proposed for the three degrees of freedom model helicopter, subject to system uncertainties, unknown external disturbances, and actuator faults. To tackle system uncertainty and nonlinear actuator faults, a neural network disturbance observer is developed based on the radial basis function neural network. The unknown external disturbance and the unknown neural network approximation errors are treated as a compound disturbance that is estimated by another nonlinear disturbance observer. A disturbance observer-based adaptive neural fault-tolerant control scheme is then developed to track the desired system output in the presence of system uncertainty, external disturbance, and actuator faults. The stability of the whole closed-loop system is analyzed using the Lyapunov method, which guarantees the convergence of all closed-loop signals. Finally, the simulation results are presented to illustrate the effectiveness of the new control design techniques.
Keywords: 3-DOF model helicopter, adaptive control; disturbance observer; fault-tolerant control; neural network
Rights: © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
RMID: 0030029199
DOI: 10.1109/TSMC.2015.2426140
Grant ID: http://purl.org/au-research/grants/arc/DP140102180
http://purl.org/au-research/grants/arc/LP140100471
http://purl.org/au-research/grants/arc/LE150100079
Appears in Collections:Electrical and Electronic Engineering publications

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