Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/100893
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Type: Journal article
Title: Approximation-based adaptive tracking control for MIMO nonlinear systems with input saturation
Author: Zhou, Q.
Shi, P.
Tian, Y.
Wang, M.
Citation: IEEE Transactions on Cybernetics, 2015; 45(10):2119-2128
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2015
ISSN: 2168-2267
2168-2275
Statement of
Responsibility: 
Qi Zhou, Peng Shi, Yang Tian, and Mingyu Wang
Abstract: In this paper, an approximation-based adaptive tracking control approach is proposed for a class of multiinput multioutput nonlinear systems. Based on the method of neural network, a novel adaptive controller is designed via backstepping design process. Furthermore, by introducing Nussbaum function, the issue of unknown control directions is handled. In the backstepping design process, the dynamic surface control technique is employed to avoid differentiating certain nonlinear functions repeatedly. Moreover, in order to reduce the number of adaptation laws, we do not use the neural networks to directly approximate the unknown nonlinear functions but the desired control signals. Finally, we provide two examples to illustrate the effectiveness of the proposed approach.
Keywords: Adaptive neural network control; backstepping approach; input saturation; multiinput multioutput (MIMO) nonlinear
Rights: © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
RMID: 0030037231
DOI: 10.1109/TCYB.2014.2365778
Grant ID: http://purl.org/au-research/grants/arc/DP140102180
http://purl.org/au-research/grants/arc/LP140100471
Appears in Collections:Electrical and Electronic Engineering publications

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