A Novel Finite-Time Control for Nonstrict Feedback Saturated Nonlinear Systems With Tracking Error Constraint

被引：174
作者：
Sun, Kangkang [1 ,2 ]
Qiu, Jianbin [1 ,2 ]
Karimi, Hamid Reza [3 ]
Gao, Huijun [1 ,2 ]
机构：
[1] Harbin Inst Technol, State Key Lab Robot & Syst, Harbin 150080, Peoples R China
[2] Harbin Inst Technol, Res Inst Intelligent Control & Syst, Harbin 150080, Peoples R China
[3] Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2021年 / 51卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Nonlinear systems; Stability analysis; Backstepping; Sun; Radial basis function networks; Closed loop systems; Error constraint; finite-time; input saturation; neural network; nonstrict feedback nonlinear systems; ADAPTIVE NEURAL-CONTROL; DYNAMIC SURFACE CONTROL; STABILIZATION; DESIGN; ODD;
D O I：
10.1109/TSMC.2019.2958072
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 article investigates the neural network-based finite-time control issue for a class of nonstrict feedback nonlinear systems, which contain unknown smooth functions, input saturation, and error constraint. Radial basis function neural networks and an auxiliary control signal are adopted to identify unknown smooth functions and deal with input saturation, respectively. The issue of error constraint is solved by combining the performance function and error transformation. Based on the backstepping recursive technique, a neural network-based finite-time control scheme is developed. The developed control scheme can ensure that the closed-loop system is semi-globally practically finite-time stable. Finally, the validity of theoretical results is verified via simulation studies.
引用
收藏
页码：3968 / 3979
页数：12
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