Force Sensorless Admittance Control With Neural Learning for Robots With Actuator Saturation

doi:10.1109/TIE.2019.2912781

	Force Sensorless Admittance Control With Neural Learning for Robots With Actuator Saturation
	Peng, Guangzhu 6; Yang, Chenguang 5; He, Wei 4; Chen, C. L. Philip 1,2,3
刊名	IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
	2020-04-01
卷号	67 期号:4 页码:3138-3148
关键词	Robot sensing systems Admittance Manipulators Force Torque Adaptation models Adaptive neural control admittance control neural networks (NNs) observer
ISSN号	0278-0046
DOI	10.1109/TIE.2019.2912781
通讯作者	Yang, Chenguang(cyang@ieee.org)
英文摘要	In this paper, we present a sensorless admittance control scheme for robotic manipulators to interact with unknown environments in the presence of actuator saturation. The external environment is defined as linear models with unknown dynamics. Using admittance control, the robotic manipulator is controlled to be compliant with external torque from the environment. The external torque acted on the end-effector is estimated by using a disturbance observer based on generalized momentum. The model uncertainties are solved by using radial basis neural networks (NNs). To guarantee the tracking performance and tackle the effect of actuator saturation, an adaptive NN controller integrating an auxiliary system is designed to handle the actuator saturation. By employing Lyapunov stability theory, the stability of the closed-loop system is achieved. The experiments on the Baxter robot are implemented to verify the effectiveness of the proposed method.
资助项目	Engineering and Physical Sciences Research Council[EP/S001913] ; National Natural Science Foundation of China[61751202] ; National Natural Science Foundation of China[61572540] ; Key Program for International S&T Cooperation Projects of China[2016YFE0121200] ; Macau Science and Technology Development Fund[019/2015/A1] ; Macau Science and Technology Development Fund[079/2017/A2] ; Macau Science and Technology Development Fund[024/2015/AMJ] ; University of Macau
WOS关键词	NONLINEAR-SYSTEMS ; IMPEDANCE CONTROL ; DESIGN
WOS研究方向	Automation & Control Systems ; Engineering ; Instruments & Instrumentation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000507307000061
资助机构	Engineering and Physical Sciences Research Council ; National Natural Science Foundation of China ; Key Program for International S&T Cooperation Projects of China ; Macau Science and Technology Development Fund ; University of Macau
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/29539]
专题	离退休人员
通讯作者	Yang, Chenguang
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China 2.Dalian Maritime Univ, Dept Nav, Dalian 116026, Peoples R China 3.Univ Macau, Fac Sci & Technol, Macau 999078, Peoples R China 4.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China 5.Univ West England, Bristol Robot Lab, Bristol BS16 1QY, Avon, England 6.Univ Macau, Dept Comp & Informat Sci, Fac Sci & Technol, Macau 999078, Peoples R China
推荐引用方式 GB/T 7714	Peng, Guangzhu,Yang, Chenguang,He, Wei,et al. Force Sensorless Admittance Control With Neural Learning for Robots With Actuator Saturation[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2020,67(4):3138-3148.
APA	Peng, Guangzhu,Yang, Chenguang,He, Wei,&Chen, C. L. Philip.(2020).Force Sensorless Admittance Control With Neural Learning for Robots With Actuator Saturation.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,67(4),3138-3148.
MLA	Peng, Guangzhu,et al."Force Sensorless Admittance Control With Neural Learning for Robots With Actuator Saturation".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 67.4(2020):3138-3148.