Adaptive Neural Network-Based Backstepping Sliding Mode Control Approach for Dual-Arm Robots

Nguyen, TV; Thai, NH; Pham, HT; Phan, TA; Nguyen, L; Le, HX; Nguyen, HD

Adaptive Neural Network-Based Backstepping Sliding Mode Control Approach for Dual-Arm Robots

Nguyen, TV

Thai, NH Pham, HT Phan, TA Nguyen, L

Le, HX Nguyen, HD

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Publication Type:: Journal Article
Citation:: Journal of Control, Automation and Electrical Systems, 2019, 30 (4), pp. 512 - 521
Issue Date:: 2019-08-15

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Field	Value	Language
dc.contributor.author	Nguyen, TV https://orcid.org/0000-0002-3246-6281	en_US
dc.contributor.author	Thai, NH	en_US
dc.contributor.author	Pham, HT	en_US
dc.contributor.author	Phan, TA	en_US
dc.contributor.author	Nguyen, L https://orcid.org/0000-0001-5360-886X	en_US
dc.contributor.author	Le, HX	en_US
dc.contributor.author	Nguyen, HD	en_US
dc.date.issued	2019-08-15	en_US
dc.identifier.citation	Journal of Control, Automation and Electrical Systems, 2019, 30 (4), pp. 512 - 521	en_US
dc.identifier.issn	2195-3880	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133005
dc.description.abstract	© 2019, Brazilian Society for Automatics--SBA. The paper introduces an adaptive strategy to effectively control a nonlinear dual-arm robot under external disturbances and uncertainties. By the use of the backstepping sliding mode control (BSSMC) method, the proposed algorithm first allows the manipulators to be able to robustly track the desired trajectories. Furthermore, due to the nonlinear, uncertain and unmodeled dynamics of the dual-arm robot, it is proposed to employ the radial basis function network (RBFN) to adaptively estimate the robot’s dynamic model. Though the estimation of the dynamics is approximate, the adaptation law is derived from the Lyapunov theory, which provides the controller with ability to guarantee stability of the whole system in spite of its nonlinearities, parameter uncertainties and external load variations. The effectiveness of the proposed RBFN–BSSMC approach is demonstrated by implementation in a simulation environment with realistic parameters, where the obtained results are highly promising.	en_US
dc.relation.ispartof	Journal of Control, Automation and Electrical Systems	en_US
dc.relation.isbasedon	10.1007/s40313-019-00472-z	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Adaptive Neural Network-Based Backstepping Sliding Mode Control Approach for Dual-Arm Robots	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	30	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	30	en_US

Abstract:

© 2019, Brazilian Society for Automatics--SBA. The paper introduces an adaptive strategy to effectively control a nonlinear dual-arm robot under external disturbances and uncertainties. By the use of the backstepping sliding mode control (BSSMC) method, the proposed algorithm first allows the manipulators to be able to robustly track the desired trajectories. Furthermore, due to the nonlinear, uncertain and unmodeled dynamics of the dual-arm robot, it is proposed to employ the radial basis function network (RBFN) to adaptively estimate the robot’s dynamic model. Though the estimation of the dynamics is approximate, the adaptation law is derived from the Lyapunov theory, which provides the controller with ability to guarantee stability of the whole system in spite of its nonlinearities, parameter uncertainties and external load variations. The effectiveness of the proposed RBFN–BSSMC approach is demonstrated by implementation in a simulation environment with realistic parameters, where the obtained results are highly promising.

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http://hdl.handle.net/10453/133005