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	Discrete Data-Driven Control of Redundant Manipulators With Adaptive Jacobian Matrix
	Liu, Mei1,2; Hu, Yafei1,2; Jin, Long1,2
	2024-01-11
摘要	Redundant manipulators are widely used in various fields due to their multiple degrees of freedom characteristics, and their tracking control is an important problem in the field of robotics. In order to control manipulators with unknown models in practical applications, this article proposes a discrete data-driven Jacobian matrix adaptive control (DDJMAC) scheme. The scheme is composed of a discrete Jacobian matrix estimator, a discrete neural dynamics controller, and a Kalman filter. Subsequently, the convergence and robustness of the DDJMAC scheme are demonstrated by theoretical analyses. Finally, simulations, comparisons, and physical experiments are performed on redundant manipulators, and the results confirm the effectiveness, superiority, and practicality of the proposed scheme.
关键词	Manipulators Mathematical models Manipulator dynamics Jacobian matrices Kalman filters Task analysis Kinematics Discrete data-driven Jacobian matrix adaptive control (DDJMAC) Kalman filter model-unknown neural dynamics redundant manipulators
DOI	10.1109/TIE.2023.3347831
发表期刊	IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
ISSN	0278-0046
页码	11
通讯作者	Jin, Long()
收录类别	SCI
WOS记录号	WOS:001165487300001
语种	en