Fixed-time sliding mode control of multi-joint robot based on RBF neural network

被引：0
|
作者：
Liu Y.-C. [1 ]
Xiong Y.-H. [1 ]
Yang H.-X. [1 ]
机构：
[1] College of Electrical Engineering, Sichuan University, Chengdu
来源：
Kongzhi yu Juece/Control and Decision | 2022年 / 37卷 / 11期
关键词：
fixed-time sliding mode; multi-joint robot; RBF neural network; trajectory tracking; virtual prototype;
D O I：
10.13195/j.kzyjc.2021.0421
中图分类号：
学科分类号：
摘要：
A fixed-time sliding mode control method based on the radial basis function (RBF) neural network is proposed for trajectory tracking control of multi-joint robots with typical nonlinear characteristics. Firstly, the dynamic model of multi-joint robots including system model uncertainty and external disturbance is established based on the Kane method. A sliding mode controller with fixed-time convergence is designed according to the dynamic model of the robot, the RBF neural network is used to approximate the uncertainties in the system model. The Lyapunov theory is used to prove that the tracking error of the system can converge in a fixed-time. Finally, a virtual prototype of a certain type of multi-joint robot is simulated and analyzed. Compared with the finite-time sliding mode controller based on the RBF neural network, the proposed controller has good tracking performance and can ensure that the system state converges in a fixed-time. © 2022 Northeast University. All rights reserved.
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页码：2790 / 2798
页数：8
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