Sliding mode neural controller for nonlinear systems with higher-order and uncertainties

Nguyen, TVM; Nguyen, HT; Ha, QP

Sliding mode neural controller for nonlinear systems with higher-order and uncertainties

Nguyen, TVM Nguyen, HT

Ha, QP

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Publication Type:: Conference Proceeding
Citation:: 2004 IEEE Conference on Robotics, Automation and Mechatronics, 2004, pp. 1026 - 1031
Issue Date:: 2004-12-01

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Field	Value	Language
dc.contributor.author	Nguyen, TVM	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.date.issued	2004-12-01	en_US
dc.identifier.citation	2004 IEEE Conference on Robotics, Automation and Mechatronics, 2004, pp. 1026 - 1031	en_US
dc.identifier.isbn	0780386469	en_US
dc.identifier.isbn	9780780386464	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2777
dc.description.abstract	In this paper, we propose a new neural controller architecture which is derived from adaptive sliding mode control framework for SISO nonlinear system with higher-order and uncertainties. This neural controller can overcome some disadvantages inherent in sliding mode controllers such as chattering problem, complex calculation of the equivalent control term and unavailable knowledge of the upper bounds of system uncertainties. Experimental results for a Coupled Drives CE8 system show that a real-time neural controller has been implemented successfully.	en_US
dc.relation	http://purl.org/au-research/grants/arc/LE0454081
dc.relation.ispartof	2004 IEEE Conference on Robotics, Automation and Mechatronics	en_US
dc.title	Sliding mode neural controller for nonlinear systems with higher-order and uncertainties	en_US
dc.type	Conference Proceeding
utslib.for	0910 Manufacturing Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
dc.location.activity	Singapore	en_US
dc.location.activity	Lugano, Switzerland
dc.location.activity	Singapore, SINGAPORE
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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, we propose a new neural controller architecture which is derived from adaptive sliding mode control framework for SISO nonlinear system with higher-order and uncertainties. This neural controller can overcome some disadvantages inherent in sliding mode controllers such as chattering problem, complex calculation of the equivalent control term and unavailable knowledge of the upper bounds of system uncertainties. Experimental results for a Coupled Drives CE8 system show that a real-time neural controller has been implemented successfully.

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http://hdl.handle.net/10453/2777