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

Peihao Du; Hongjing Liang; Shiyi Zhao; Choon Ki Ahn

doi:10.1109/TSMC.2019.2918351

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

Peihao Du, Hongjing Liang, Shiyi Zhao, Choon Ki Ahn

Research output: Contribution to journal › Article › peer-review

140 Citations (Scopus)

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.

Original language	English
Article number	8736293
Pages (from-to)	3136-3147
Number of pages	12
Journal	IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume	51
Issue number	5
DOIs	https://doi.org/10.1109/TSMC.2019.2918351
Publication status	Published - 2021 May

Keywords

Finite time
input saturation
neural network (NN)
nonlinear large-scale systems
time-varying output constraints

ASJC Scopus subject areas

Software
Control and Systems Engineering
Human-Computer Interaction
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TSMC.2019.2918351

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title = "Neural-Based Decentralized Adaptive Finite-Time Control for Nonlinear Large-Scale Systems with Time-Varying Output Constraints",

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. ",

keywords = "Finite time, input saturation, neural network (NN), nonlinear large-scale systems, time-varying output constraints",

author = "Peihao Du and Hongjing Liang and Shiyi Zhao and Ahn, {Choon Ki}",

note = "Funding Information: Manuscript received March 4, 2019; accepted May 10, 2019. Date of publication June 13, 2019; date of current version April 15, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61703051, in part by the Department of Education of Liaoning Province under Grant LZ2017001, and in part by the National Research Foundation of Korea through the Ministry of Science, ICT and Future Planning under Grant NRF-2017R1A1A1A05001325. This paper was recommended by Associate Editor J. Sarangapani. (Corresponding authors: Hongjing Liang; Choon Ki Ahn.) P. Du and S. Zhao are with the School of Mathematics and Physics, Bohai University, Jinzhou 121013, China (e-mail: peihaodu2017@163.com; shiyizhao2016@163.com). Publisher Copyright: {\textcopyright} 2013 IEEE.",

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doi = "10.1109/TSMC.2019.2918351",

language = "English",

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AU - Ahn, Choon Ki

N1 - Funding Information: Manuscript received March 4, 2019; accepted May 10, 2019. Date of publication June 13, 2019; date of current version April 15, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61703051, in part by the Department of Education of Liaoning Province under Grant LZ2017001, and in part by the National Research Foundation of Korea through the Ministry of Science, ICT and Future Planning under Grant NRF-2017R1A1A1A05001325. This paper was recommended by Associate Editor J. Sarangapani. (Corresponding authors: Hongjing Liang; Choon Ki Ahn.) P. Du and S. Zhao are with the School of Mathematics and Physics, Bohai University, Jinzhou 121013, China (e-mail: peihaodu2017@163.com; shiyizhao2016@163.com). Publisher Copyright: © 2013 IEEE.

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

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