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Discrete-time Optimal Adaptive RBFNN Control for Robot Manipulators with Uncertain Dynamics

Runxian Yang, Chenguang Yang, Mou Chen, Andy SK Annamalai

Neurocomputing, Volume: 234, Pages: 107 - 115

Swansea University Author: Chenguang Yang

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DOI (Published version): 10.1016/j.neucom.2016.12.048

Abstract

In this paper, a novel optimal adaptive radial basis function neural network (RBFNN) control has been investigated for a class of multiple-input-multiple-output (MIMO) nonlinear robot manipulators with uncertain dynamics in discrete time. To facilitate digital implementations of the robot controller...

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Published in: Neurocomputing
ISSN: 0925-2312
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa31618
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spelling 2017-02-14T14:37:47.7367245 v2 31618 2017-01-11 Discrete-time Optimal Adaptive RBFNN Control for Robot Manipulators with Uncertain Dynamics d2a5024448bfac00a9b3890a8404380b Chenguang Yang Chenguang Yang true false 2017-01-11 EEN In this paper, a novel optimal adaptive radial basis function neural network (RBFNN) control has been investigated for a class of multiple-input-multiple-output (MIMO) nonlinear robot manipulators with uncertain dynamics in discrete time. To facilitate digital implementations of the robot controller, a robot model in discrete time has been employed. A high order uncertain robot model is able to be transformed to a predictor form, and a feedback control system has been then developed without noncausal problem in discrete time. The controller has been designed by an adaptive neural network (NN) based on the feedback system. The adaptive RBFNN robot control system has been investigated by a critic RBFNN and an actor RBFNN to approximate a desired control and a strategic utility function, respectively. The rigorous Lyapunov analysis is used to establish uniformly ultimate boundedness (UUB) of closed-loop signals, and the high-quality dynamic performance against uncertainties and disturbances is obtained by appropriately selecting the controller parameters. Simulation studies validate that the proposed control scheme has performed better than other available methods currently, for robot manipulators. Journal Article Neurocomputing 234 107 115 0925-2312 Discrete-time system; Neural networks; Robot manipulator; Adaptive control; Dynamics uncertainties 19 4 2017 2017-04-19 10.1016/j.neucom.2016.12.048 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2017-02-14T14:37:47.7367245 2017-01-11T12:21:11.0677466 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Runxian Yang 1 Chenguang Yang 2 Mou Chen 3 Andy SK Annamalai 4 0031618-02022017123053.pdf NEUCOM-D-16-00406_sourcefile_latex_revisition1228.pdf 2017-02-02T12:30:53.9300000 Output 8741219 application/pdf Accepted Manuscript true 2017-12-22T00:00:00.0000000 false
title Discrete-time Optimal Adaptive RBFNN Control for Robot Manipulators with Uncertain Dynamics
spellingShingle Discrete-time Optimal Adaptive RBFNN Control for Robot Manipulators with Uncertain Dynamics
Chenguang Yang
title_short Discrete-time Optimal Adaptive RBFNN Control for Robot Manipulators with Uncertain Dynamics
title_full Discrete-time Optimal Adaptive RBFNN Control for Robot Manipulators with Uncertain Dynamics
title_fullStr Discrete-time Optimal Adaptive RBFNN Control for Robot Manipulators with Uncertain Dynamics
title_full_unstemmed Discrete-time Optimal Adaptive RBFNN Control for Robot Manipulators with Uncertain Dynamics
title_sort Discrete-time Optimal Adaptive RBFNN Control for Robot Manipulators with Uncertain Dynamics
author_id_str_mv d2a5024448bfac00a9b3890a8404380b
author_id_fullname_str_mv d2a5024448bfac00a9b3890a8404380b_***_Chenguang Yang
author Chenguang Yang
author2 Runxian Yang
Chenguang Yang
Mou Chen
Andy SK Annamalai
format Journal article
container_title Neurocomputing
container_volume 234
container_start_page 107
publishDate 2017
institution Swansea University
issn 0925-2312
doi_str_mv 10.1016/j.neucom.2016.12.048
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description In this paper, a novel optimal adaptive radial basis function neural network (RBFNN) control has been investigated for a class of multiple-input-multiple-output (MIMO) nonlinear robot manipulators with uncertain dynamics in discrete time. To facilitate digital implementations of the robot controller, a robot model in discrete time has been employed. A high order uncertain robot model is able to be transformed to a predictor form, and a feedback control system has been then developed without noncausal problem in discrete time. The controller has been designed by an adaptive neural network (NN) based on the feedback system. The adaptive RBFNN robot control system has been investigated by a critic RBFNN and an actor RBFNN to approximate a desired control and a strategic utility function, respectively. The rigorous Lyapunov analysis is used to establish uniformly ultimate boundedness (UUB) of closed-loop signals, and the high-quality dynamic performance against uncertainties and disturbances is obtained by appropriately selecting the controller parameters. Simulation studies validate that the proposed control scheme has performed better than other available methods currently, for robot manipulators.
published_date 2017-04-19T03:38:38Z
_version_ 1763751722548723712
score 11.017797

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