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

Lepeng Chen, Rongxin Cui, Chenguang Yang, Weisheng Yan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages660-665
Number of pages6
ISBN (Electronic)9781728100647
DOIs
StatePublished - Jul 2019
Event4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019 - Osaka, Japan
Duration: 3 Jul 20195 Jul 2019

Publication series

Name2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019

Conference

Conference4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019
Country/TerritoryJapan
CityOsaka
Period3/07/195/07/19

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