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

Lepeng Chen; Rongxin Cui; Chenguang Yang; Weisheng Yan

doi:10.1109/ICARM.2019.8834000

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

Lepeng Chen, Rongxin Cui, Chenguang Yang, Weisheng Yan

School of Marine Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	660-665
Number of pages	6
ISBN (Electronic)	9781728100647
DOIs	https://doi.org/10.1109/ICARM.2019.8834000
State	Published - Jul 2019
Event	4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019 - Osaka, Japan Duration: 3 Jul 2019 → 5 Jul 2019

Publication series

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

Conference

Conference	4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019
Country/Territory	Japan
City	Osaka
Period	3/07/19 → 5/07/19

Access to Document

10.1109/ICARM.2019.8834000

Cite this

Chen, L., Cui, R., Yang, C., & Yan, W. (2019). Global neural learning finite-time control of robot manipulators with guaranteed transient tracking performance. In 2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019 (pp. 660-665). Article 8834000 (2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICARM.2019.8834000

Chen, Lepeng ; Cui, Rongxin ; Yang, Chenguang et al. / Global neural learning finite-time control of robot manipulators with guaranteed transient tracking performance. 2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 660-665 (2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019).

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title = "Global neural learning finite-time control of robot manipulators with guaranteed transient tracking performance",

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.",

author = "Lepeng Chen and Rongxin Cui and Chenguang Yang and Weisheng Yan",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019 ; Conference date: 03-07-2019 Through 05-07-2019",

year = "2019",

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doi = "10.1109/ICARM.2019.8834000",

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series = "2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Chen, L, Cui, R, Yang, C & Yan, W 2019, Global neural learning finite-time control of robot manipulators with guaranteed transient tracking performance. in 2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019., 8834000, 2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019, Institute of Electrical and Electronics Engineers Inc., pp. 660-665, 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019, Osaka, Japan, 3/07/19. https://doi.org/10.1109/ICARM.2019.8834000

Global neural learning finite-time control of robot manipulators with guaranteed transient tracking performance. / Chen, Lepeng; Cui, Rongxin; Yang, Chenguang et al.
2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 660-665 8834000 (2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Chen L, Cui R, Yang C, Yan W. Global neural learning finite-time control of robot manipulators with guaranteed transient tracking performance. In 2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 660-665. 8834000. (2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019). doi: 10.1109/ICARM.2019.8834000

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

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