Predictor-Based Fixed-Time Neural Dynamics Surface Tracking Control for Nonlinear Systems With Unknown Backlash-Like Hysteresis

被引：0
作者：
Zhang, Huaguang [1 ,2 ]
Ma, Jiawei [3 ]
Zhang, Juan [3 ]
Wang, Le [3 ]
机构：
[1] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110004, Liaoning, Peoples R China
[2] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Liaoning, Peoples R China
[3] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2025年 / 55卷 / 02期
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Dynamics surface control (DSC); fixed-time theory; neural networks; predictor-based; ADAPTIVE-CONTROL; STABILIZATION;
D O I：
10.1109/TSMC.2024.3505152
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The issue of predictor-based neural fixed-time dynamic surface control for the nonlinear systems with unknown backlash-like hysteresis is the research focus of this article. By applying the predictor-based neural control scheme, the system nonlinear functions can be smoothly estimated. In addition, an improved dynamics surface is proposed to decrease the difficulty of the controller design procedure while ensuring that the dynamic surface compensating signals can satisfy the fixed-time stability. Further, on the basis of fixed-time theorem and backstepping control technology, the designed controller can ensure all signals of the considered closed-loop systems are fixed-time bounded in the presence of unknown backlash-like hysteresis. Eventually, the simulation cases are given to imply the effectiveness of the designed method.
引用
收藏
页码：1506 / 1515
页数：10
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