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Type: Journal article
Title: Neuroadaptive Finite-Time Control for Nonlinear MIMO Systems With Input Constraint
Author: Yu, J.
Shi, P.
Liu, J.
Lin, C.
Citation: IEEE Transactions on Cybernetics, 2022; 52(7):6676-6683
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Issue Date: 2022
ISSN: 2168-2267
Statement of
Jinpeng Yu, Peng Shi, Fellow, IEEE, Jiapeng Liu, and Chong Lin, Senior Member, IEEE
Abstract: This article considers the problem of finite-time (FT) tracking control for a class of uncertain multi-input–multioutput (MIMO) nonlinear systems with input backlash. A modified FT command filter is designed in each step of backstepping, which ensures the output of the filter can faster approximate the derivatives of virtual signals, suppress chattering, and relax the input signal limit of the Levant differentiator. Then, the corresponding improved FT error compensation mechanism is adopted to reduce the negative impact of filtering errors. Furthermore, a neural-network-adaptive technology is proposed for MIMO systems with input backlash via FT convergence. It is shown that desired tracking performance can be implemented in finite time. The simulation example is presented to illustrate the effectiveness and advantages of the new design method.
Keywords: Adaptive neural network (NN) control; backstepping; finite-time (FT) convergence; input backlash
Rights: © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
DOI: 10.1109/TCYB.2020.3032530
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Appears in Collections:Computer Science publications

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