Adaptive RBFNN control of robot manipulators with finite-time convergence

Jing Na; Chenguang Yang; Runxian Yang

doi:10.1109/IECON.2016.7792980

Adaptive RBFNN control of robot manipulators with finite-time convergence

Jing Na, Chenguang Yang, Runxian Yang

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

6 Citations (Scopus)

390 Downloads (Pure)

Abstract

In this paper, the position tracking control with finite-time convergence has been studied for a class of nonlinear uncertain robot manipulators. Radial basis function neural network (RBFNN) based adaptive control is designed to compensate for the effect of the unknown dynamics. To achieve the finite-time convergence of both trajectory tracking error and RBFNN learning error, barrier Lyapunov functions (BLFs) and and filtering techniques are employed to design a performance function and a tracking error region to ensure position tracking error converge to a pair of specified bounds in a finite time. The effectiveness and efficiency of the proposed control method is tested and verified by simulation studies.

Original language	English
Title of host publication	IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society
Subtitle of host publication	Proceedings of a meeting held 24-27 October 2016, Florence, Italy
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	42-47
Number of pages	6
ISBN (Electronic)	9781509034741
ISBN (Print)	9781509034758
DOIs	https://doi.org/10.1109/IECON.2016.7792980
Publication status	Published - Mar 2017
Event	IECON 2016 : 42nd Annual Conference of the IEEE Industrial Electronics Society - Palazzo dei Congressi, Piazza Adua, 1, Florence, Italy Duration: 24 Oct 2016 → 27 Oct 2016 http://www.iecon2016.org/

Conference

Conference	IECON 2016
Country/Territory	Italy
City	Florence
Period	24/10/16 → 27/10/16
Internet address	http://www.iecon2016.org/

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10.1109/IECON.2016.7792980

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Na, J., Yang, C., & Yang, R. (2017). Adaptive RBFNN control of robot manipulators with finite-time convergence. In IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society : Proceedings of a meeting held 24-27 October 2016, Florence, Italy (pp. 42-47). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IECON.2016.7792980

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title = "Adaptive RBFNN control of robot manipulators with finite-time convergence",

abstract = "In this paper, the position tracking control with finite-time convergence has been studied for a class of nonlinear uncertain robot manipulators. Radial basis function neural network (RBFNN) based adaptive control is designed to compensate for the effect of the unknown dynamics. To achieve the finite-time convergence of both trajectory tracking error and RBFNN learning error, barrier Lyapunov functions (BLFs) and and filtering techniques are employed to design a performance function and a tracking error region to ensure position tracking error converge to a pair of specified bounds in a finite time. The effectiveness and efficiency of the proposed control method is tested and verified by simulation studies.",

author = "Jing Na and Chenguang Yang and Runxian Yang",

year = "2017",

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doi = "10.1109/IECON.2016.7792980",

language = "English",

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booktitle = "IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "IECON 2016 : 42nd Annual Conference of the IEEE Industrial Electronics Society ; Conference date: 24-10-2016 Through 27-10-2016",

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Na, J, Yang, C & Yang, R 2017, Adaptive RBFNN control of robot manipulators with finite-time convergence. in IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society : Proceedings of a meeting held 24-27 October 2016, Florence, Italy. Institute of Electrical and Electronics Engineers (IEEE), pp. 42-47, IECON 2016 , Florence, Italy, 24/10/16. https://doi.org/10.1109/IECON.2016.7792980

Adaptive RBFNN control of robot manipulators with finite-time convergence. / Na, Jing; Yang, Chenguang; Yang, Runxian.
IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society : Proceedings of a meeting held 24-27 October 2016, Florence, Italy. Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 42-47.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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AU - Na, Jing

AU - Yang, Chenguang

AU - Yang, Runxian

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N2 - In this paper, the position tracking control with finite-time convergence has been studied for a class of nonlinear uncertain robot manipulators. Radial basis function neural network (RBFNN) based adaptive control is designed to compensate for the effect of the unknown dynamics. To achieve the finite-time convergence of both trajectory tracking error and RBFNN learning error, barrier Lyapunov functions (BLFs) and and filtering techniques are employed to design a performance function and a tracking error region to ensure position tracking error converge to a pair of specified bounds in a finite time. The effectiveness and efficiency of the proposed control method is tested and verified by simulation studies.

AB - In this paper, the position tracking control with finite-time convergence has been studied for a class of nonlinear uncertain robot manipulators. Radial basis function neural network (RBFNN) based adaptive control is designed to compensate for the effect of the unknown dynamics. To achieve the finite-time convergence of both trajectory tracking error and RBFNN learning error, barrier Lyapunov functions (BLFs) and and filtering techniques are employed to design a performance function and a tracking error region to ensure position tracking error converge to a pair of specified bounds in a finite time. The effectiveness and efficiency of the proposed control method is tested and verified by simulation studies.

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DO - 10.1109/IECON.2016.7792980

M3 - Conference Contribution (Conference Proceeding)

SN - 9781509034758

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EP - 47

BT - IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - IECON 2016

Y2 - 24 October 2016 through 27 October 2016

ER -

Adaptive RBFNN control of robot manipulators with finite-time convergence

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