Neural Adaptive Impedance Control for Force Tracking in Uncertain Environment

Autor:	Hao An, Chao Ye, Zikang Yin, Weiyang Lin
Rok vydání:	2023
Předmět:	neural adaptive impedance control Computer Networks and Communications Hardware and Architecture Control and Systems Engineering Signal Processing friction identification force tracking Electrical and Electronic Engineering online optimization
Zdroj:	Electronics Volume 12 Issue 3 Pages: 640
ISSN:	2079-9292
DOI:	10.3390/electronics12030640
Popis:	Torque-based impedance control, a kind of classical active compliant control, is widely required in human–robot interaction, medical rehabilitation, and other fields. Adaptive impedance control effectively tracks the force when the robot comes in contact with an unknown environment. Conventional adaptive impedance control (AIC) introduces the force tracking error of the last moment to adjust the controller parameters online, which is an indirect method. In this paper, joint friction in the robot system is first identified and compensated for to enable the excellent performance of torque-based impedance control. Second, neural networks are inserted into the torque-based impedance controller, and a neural adaptive impedance control (NAIC) scheme with directly online optimized parameters is proposed. In addition, NAIC can be deployed directly without the need for data collection and training. Simulation studies and real-world experiments with a six link rotary robot manipulator demonstrate the excellent performance of NAIC.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::866b2f0e04f47be1b6f79e03634fcc2d https://doi.org/10.3390/electronics12030640 Zobrazit plný text záznamu