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Neural-Based Decentralized Adaptive Finite-Time Control for Nonlinear Large-Scale Systems With Time-Varying Output Constraints

Authors
Du, PeihaoLiang, HongjingZhao, ShiyiAhn, Choon Ki
Issue Date
5월-2021
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Time-varying systems; Nonlinear systems; Adaptive systems; Large-scale systems; Stability analysis; Artificial neural networks; Lyapunov methods; Finite time; input saturation; neural network (NN); nonlinear large-scale systems; time-varying output constraints
Citation
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, v.51, no.5, pp.3136 - 3147
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Volume
51
Number
5
Start Page
3136
End Page
3147
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/128071
DOI
10.1109/TSMC.2019.2918351
ISSN
2168-2216
Abstract
This paper addresses the adaptive finite-time decentralized control problem for time-varying output-constrained nonlinear large-scale systems preceded by input saturation. The intermediate control functions designed are approximated by neural networks. Time-varying barrier Lyapunov functions are used to ensure that the system output constraints are never breached. An adaptive finite-time decentralized control scheme is devised by combining the backstepping approach with Lyapunov function theory. Under the action of the proposed approach, the system stability and desired control performance can be obtained in finite time. The feasibility of this control strategy is demonstrated by using simulation results.
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