Adaptive radial basis function neural network control of a wire-driven parallel robot based on local model approximation

被引：0

作者：

Wang Y.-Q. ^{[1
]}

Lin Q. ^{[2
]}

Zhou L. ^{[1
]}

Shi X.-X. ^{[1
]}

Du Y.-J. ^{[1
]}

Qiao G.-F. ^{[1
]}

机构：

[1] School of Automation, Nanjing Institute of Technology, Nanjing

[2] School of Aerospace Engineering, Xiamen University, Xiamen

来源：

Wang, Yu-Qi (940898628@qq.com) | 1600年 / South China University of Technology卷 / 38期

基金：

中国国家自然科学基金;

关键词：

Adaptive control; Local model approximation; Lyapunov function; RBF neural network; Wire-driven parallel robot;

D O I：

10.7641/CTA.2020.00184

中图分类号：

学科分类号：

摘要：

In order to ensure the pose of the end effector of wind tunnel test wire-driven parallel robot (WTT-WDPR), an adaptive RBF neural network control based on local model approximation is proposed. The dynamic equation of the aircraft model is established by Newton Euler method, and the dynamic equation of the driving system is established based on the dynamic moment balance equation. In this paper, RBF neural network is used to design the approximation of local model and control law, and Lyapunov function is constructed to analyze the stability of the system. The results show that WTT-WDPR tends to be asymptotically stable. The WTT-WDPR is simulated by MATLAB/Simulink. The simulation results verify the correctness and feasibility of the designed adaptive RBF neural network control, meet the requirements of system control accuracy, and lay a theoretical foundation for practical application and technical realization on the prototype. © 2021, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.

引用

页码：380 / 390

页数：10

共 24 条

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