Global neural learning finite-time control of robot manipulators with guaranteed transient tracking performance

Lepeng Chen, Rongxin Cui, Chenguang Yang, Weisheng Yan

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

A global neural learning tracking control for robot manipulators to guarantee the transient performance and finite-time convergence is investigated. The paper develops some auxiliary filtered variables and a nonsingular terminal slide mode (NTSM) surface to guarantee the tracking error converging to zero with finite time. An error transformation function is also introduced to deduce the control law, which ensure that the transient tracking error never violate the predefined boundedness. Furthermore, under the persistent excitation (PE) condition, the weights of neural networks (NNs) will converge to optimal values with finite time, which can be reused to decrease the computational cost. Compared experimental results are carried out to verify the superior performance of our controller.

源语言英语
主期刊名2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019
出版商Institute of Electrical and Electronics Engineers Inc.
660-665
页数6
ISBN(电子版)9781728100647
DOI
出版状态已出版 - 7月 2019
活动4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019 - Osaka, 日本
期限: 3 7月 20195 7月 2019

出版系列

姓名2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019

会议

会议4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019
国家/地区日本
Osaka
时期3/07/195/07/19

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