Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/130932
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Type: Journal article
Title: Adaptive neural command filtering control for nonlinear MIMO systems with saturation input and unknown control direction
Author: Yu, J.
Shi, P.
Lin, C.
Yu, H.
Citation: IEEE Transactions on Cybernetics, 2020; 50(6):2536-2545
Publisher: IEEE
Issue Date: 2020
ISSN: 2168-2267
2168-2275
Statement of
Responsibility: 
Jinpeng Yu, Peng Shi, Chong Lin and Haisheng Yu
Abstract: In this paper, the tracking control problem is considered for a class of multiple-input multiple-output (MIMO) nonlinear systems with input saturation and unknown direction control gains. A command filtered adaptive neural networks (NNs) control method is presented with regard to the MIMO systems by designing the virtual controllers and error compensation signals. First, the command filtering is used to solve the "explosion of complexity" problem in the conventional backstepping design and the nonlinearities are approximated by NNs. Then, the error compensation signals are developed to conquer the shortcoming of the dynamic surface method. In addition, the Nussbaum-type functions are utilized to cope with the unknown direction control gains. The effectiveness of the proposed new design scheme is illustrated by simulation examples.
Keywords: Adaptive neural control; command filtering control; multiple-input multiple-output (MIMO) nonlinear systems; Nussbaum functions
Rights: © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
DOI: 10.1109/TCYB.2019.2901250
Grant ID: http://purl.org/au-research/grants/arc/DP170102644
Appears in Collections:Aurora harvest 8
Electrical and Electronic Engineering publications

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