Vibration Control Based on Reinforcement Learning for a Single-link Flexible Robotic Manipulator

被引：5
|
作者：
Ouyang, Yuncheng [1 ,2 ]
He, Wei [3 ]
Li, Xiajing [4 ]
Liu, Jin-Kun [5 ]
Li, Guang [6 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Sichuan, Peoples R China
[2] Univ Elect Sci & Technol China, Ctr Robot, Chengdu 611731, Sichuan, Peoples R China
[3] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
[4] Delft Univ Technol, Delft Ctr Syst & Control, NL-2628 CD Delft, Netherlands
[5] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
[6] Queen Mary Univ London, Sch Engn & Mat Sci, Mile End Rd, London E1 4NS, England
来源：
IFAC PAPERSONLINE | 2017年 / 50卷 / 01期
基金：
中国国家自然科学基金; 北京市自然科学基金;
关键词：
Neural Networks; Vibration Control; Flexible Robotic Manipulator; Assumed Mode Method; Reinforcement Learning;
D O I：
10.1016/j.ifacol.2017.08.932
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we focus on the reinforcement learning control of a single-link flexible manipulator and attempt to suppress the vibration due to its flexibility and lightweight structure. The assumed mode method (AMM) and the Lagranges equation are adopted in modeling to enhance the satisfaction of precision. Two radial basis function neural networks (RBFNNs) are employed in the designed control algorithm, actor neural network (NN) for generating a policy and critic NN for evaluating the cost-function. Rigorous stability of the system has been proven via Lyapunovs direct method. According to the performance of simulation for the proposed control scheme, the superiority and feasibility of the proposed controller is verified. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：3476 / 3481
页数：6
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