Finite-time adaptive tracking control of uncertain strict-feedback systems with nonconvex input constraint

被引：0
作者：
具有非凸输入约束的不确定严格反馈系统有限时间自适应跟踪控制
机构：
[1] School of Automation Science and Electrical Engineering, Beihang University, Beijing
来源：
Li, Ming-Xing (lmx196@126.com) | 2025年 / 40卷 / 02期
关键词：
adaptive neural network; backstepping design; dynamic surface control; finite-time stability; nonconvex input constraint; uncertain strict-feedback system;
D O I：
10.13195/j.kzyjc.2024.0110
中图分类号：
学科分类号：
摘要：
A finite-time adaptive neural network dynamic surface tracking control scheme is proposed for a class of uncertain multiple-input multiple-output strict-feedback nonlinear systems with nonconvex input constraint and external disturbance. Firstly, by introducing a nonconvex constraint operator, the designed feedback control input is transformed into the actual input vector with the largest value in the same direction, thus the actual control input is always kept in the nonconvex constraint set. Secondly, the radial basis neural network is used to approximate the uncertain continuous function vector to solve the control problem with unknown upper and lower bounds of the control gain matrix, and the inequality reduction is utilized to deal with the unknown bounded disturbance. Then, a finite-time adaptive dynamic surface tracking controller using the backstepping approach is proposed to ensure that all signals of the closed-loop system are ultimately uniformly bounded, and to realize the finite-time tracking control of the desired trajectory. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed control scheme. © 2025 Northeast University. All rights reserved.
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页码：608 / 616
页数：8
相关论文
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