ADAPTIVE BOUNDARY CONTROL VIBRATION SUPPRESSION OF FLEXIBLE MANIPULATOR BASED ON IMPROVED RBF NEURAL NETWORK

被引：0

作者：

Zheng, Qingchun ^{[1
,2
]}

Wei, Zhiyong ^{[1
,2
,3
]}

Zhu, Peihao ^{[1
,2
]}

Ma, Wenpeng ^{[1
,2
]}

Deng, Jieyong ^{[4
]}

机构：

[1] Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, Tianjin University of Technology, Tianjin,300384, China

[2] National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, (Tianjin University of Technology), China

[3] School of Mechanical Engineering, Tianjin University of Technology, Tianjin,300384, China

[4] Jiangxi Technical College of Manufacturing, Jiangxi, Nanchang,330095, China

来源：

UPB Scientific Bulletin, Series D: Mechanical Engineering | 2024年 / 86卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Adaptive Control - Boundary controls - High flexibility - Industrial fields - Low-power consumption - Lower-power consumption - Lyapunov's methods - Partial differential equation models - Radial basis function neural networks (RBF) - Vibration suppression;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

With lighter weight, lower power consumption, and higher flexibility, flexible manipulators have been widely used in industrial fields. However, it is easy to produce elastic deformation, resulting in vibration. In this paper, an improved radial basis function (RBF) neural network (NN) for adaptive boundary control is proposed. The partial differential equation (PDE) model of the flexible manipulator is established, and the asymptotic stability of the system is proved by the first Lyapunov method. Finally, simulation results show that the proposed control method can eliminate the low-amplitude angular fluctuation and the high-amplitude angular velocity fluctuation. © 2024, Politechnica University of Bucharest. All rights reserved.

引用

页码：3 / 18

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