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

被引：185

作者：

Du, Peihao ^{[1
]}

Liang, Hongjing ^{[2
]}

Zhao, Shiyi ^{[1
]}

Ahn, Choon Ki ^{[3
]}

机构：

[1] Bohai Univ, Sch Math & Phys, Jinzhou 121013, Peoples R China

[2] Bohai Univ, Coll Engn, Jinzhou 121013, Peoples R China

[3] Korea Univ, Sch Elect Engn, Seoul 136701, South Korea

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2021年 / 51卷 / 05期

基金：

新加坡国家研究基金会; 中国国家自然科学基金;

关键词：

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; TRACKING CONTROL; NETWORK CONTROL; STABILIZATION;

D O I：

10.1109/TSMC.2019.2918351

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：3136 / 3147

页数：12

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