Learning Control for Robotic Manipulator with Free Energy

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 机器人学研究室

	Learning Control for Robotic Manipulator with Free Energy
	Hu YZ(胡亚洲)1,2,3; Wang WX(王文学)1,2; Yu P(于鹏)1,2; Yao C(姚辰)1,2; Liu LQ(刘连庆)1,2
	2020
会议日期	July 27-29, 2020
会议地点	Shenyang, China
关键词	Evolving Neural Network Free Energy Principle Robotic Tracking Control Reinforcement Learning
页码	1903-1908
英文摘要	Although model-free reinforcement learning methods are illustrated to own abilities to learn a wide range of control and decision-making tasks, it is time-consuming and needs a huge number of samples to learn optimal policy. Therefore, model-based reinforcement learning algorithms are always treated as the most efficient ways to settle robotic control problems. In this work, a model-based reinforcement learning tracking control method with free energy is proposed. The control objective is to make sure that the proposed controller can let a robotic manipulator with parameters uncertainties and disturbance follow the desired trajectory and obtain good tracking performance. The main contribution of this work is to employ the free energy principle to optimize the problem of robotic control. The probability distribution of robotic dynamic model is learnt by a evolving neural network, a free energy based reinforcement learning control method uses the learnt model to find optimal control policy. The experiment results demonstrate that the proposed tracking control method is effective in solving robotic tracking control problems.
源文献作者	Systems Engineering Society of China (SESC) ; Technical Committee on Control Theory (TCCT) of Chinese Association of Automation (CAA)
产权排序	1
会议录	Proceedings of the 39th Chinese Control Conference, CCC 2020
会议录出版者	IEEE Computer Society
会议录出版地	Washington, USA
语种	英语
ISSN号	1934-1768
ISBN号	978-9-8815-6390-3
WOS记录号	WOS:000629243502005
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/27701]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Hu YZ(胡亚洲)
作者单位	1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016,P. R. China 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, P. R. China 3.University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
推荐引用方式 GB/T 7714	Hu YZ,Wang WX,Yu P,et al. Learning Control for Robotic Manipulator with Free Energy[C]. 见:. Shenyang, China. July 27-29, 2020.