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

共 50 条

[31] Rotation Angle Control Strategy for Telescopic Flexible Manipulator Based on a Combination of Fuzzy Adjustment and RBF Neural Network
Shang, Dongyang
Li, Xiaopeng
Yin, Meng
Li, Fanjie
Wen, Bangchun
CHINESE JOURNAL OF MECHANICAL ENGINEERING, 2022, 35 (01)
[32] Control Method of Flexible Manipulator Servo System Based on a Combination of RBF Neural Network and Pole Placement Strategy
Shang, Dongyang
Li, Xiaopeng
Yin, Meng
Li, Fanjie
MATHEMATICS, 2021, 9 (08)
[33] Adaptive Neural Control of a Rotating Flexible Manipulator
Maouche, A. R.
Attari, M.
2008 INTERNATIONAL SYMPOSIUM ON POWER ELECTRONICS, ELECTRICAL DRIVES, AUTOMATION AND MOTION, VOLS 1-3, 2008, : 511 - 516
[34] Boundary vibration control for a flexible Timoshenko robotic manipulator
He, Xiuyu
He, Wei
Qin, Hui
Sun, Changyin
IET CONTROL THEORY AND APPLICATIONS, 2018, 12 (07): : 875 - 882
[35] Boundary Vibration Control of a Flexible Manipulator with External Disturbances
Zheng, Xiaohui
He, Gang
Liu, Yu
2019 IEEE 4TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM 2019), 2019, : 672 - 677
[36] The impact dynamics analysis of flexible space manipulator capturing a satellite and neural network calming motion control and vibration suppression
Dong, Qiuhuang
Chen, Li
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 2014, 50 (09): : 34 - 42
[37] Neural network model based control of a flexible link manipulator
Song, BJ
Koivo, AJ
1998 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-4, 1998, : 812 - 817
[38] Neural-network-based adaptive predictive control for vibration suppression of smart structures
Jha, R
He, CL
SMART MATERIALS AND STRUCTURES, 2002, 11 (06) : 909 - 916
[39] A Novel Adaptive Sliding Mode Control of Robot Manipulator Based on RBF Neural Network and Exponential Convergence Observer
Li, Xiaoling
Gao, Hongliang
Xiong, Lang
Zhang, Hongcong
Li, Bing
NEURAL PROCESSING LETTERS, 2023, 55 (07) : 10037 - 10052
[40] A Novel Adaptive Sliding Mode Control of Robot Manipulator Based on RBF Neural Network and Exponential Convergence Observer
Xiaoling Li
Hongliang Gao
Lang Xiong
Hongcong Zhang
Bing Li
Neural Processing Letters, 2023, 55 : 10037 - 10052

← 1 2 3 4 5 →