Robust multivariable strategy and its application to a powered wheelchair

Nguyen, N; Nguyen, HT; Su, S

Robust multivariable strategy and its application to a powered wheelchair

Nguyen, N

Nguyen, HT

Su, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, 2009, pp. 7114 - 7117
Issue Date:: 2009-01-01

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Field	Value	Language
dc.contributor.author	Nguyen, N https://orcid.org/0000-0002-9958-9876	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Su, S https://orcid.org/0000-0002-5720-8852	en_US
dc.date.issued	2009-01-01	en_US
dc.identifier.citation	Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, 2009, pp. 7114 - 7117	en_US
dc.identifier.isbn	9781424432967	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11133
dc.description.abstract	The paper proposes a systematic robust multivariable control strategy based on combination of systematic triangularization technique and robust control strategies. Two design stages are required. In the first design stage, multivariable control problem is reduced into a series of scalar control problems via triangularization technique. For each specific scalar system, two advanced control strategies are proposed and implemented in the second design stage. The first one is based on Model Predictive Control, which is an iterative, finite horizon optimization procedure. The second control strategy is known as Neuro-Sliding Mode Control, which integrates Sliding Mode Control (SMC) and Neural Network Design to achieve both chattering-free and system robustness. Real-time implementation on a powered wheelchair system confirms that robustness and desired performance of a multivariable system under model uncertainties and unknown external disturbances can indeed be achieved by the combination of triangularization technique and Neuro-Sliding Mode Control. ©2009 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0666942
dc.relation	http://purl.org/au-research/grants/arc/LE0668541
dc.relation.ispartof	Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009	en_US
dc.relation.isbasedon	10.1109/IEMBS.2009.5332879	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Multivariate Analysis	en_US
dc.subject.mesh	Equipment Design	en_US
dc.subject.mesh	Wheelchairs	en_US
dc.subject.mesh	Biomedical Engineering	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.subject.mesh	Electric Power Supplies	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	Robust multivariable strategy and its application to a powered wheelchair	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
dc.location.activity	Minneapolis, Minnesota, USA	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.publication-status	Published	en_US

Abstract:

The paper proposes a systematic robust multivariable control strategy based on combination of systematic triangularization technique and robust control strategies. Two design stages are required. In the first design stage, multivariable control problem is reduced into a series of scalar control problems via triangularization technique. For each specific scalar system, two advanced control strategies are proposed and implemented in the second design stage. The first one is based on Model Predictive Control, which is an iterative, finite horizon optimization procedure. The second control strategy is known as Neuro-Sliding Mode Control, which integrates Sliding Mode Control (SMC) and Neural Network Design to achieve both chattering-free and system robustness. Real-time implementation on a powered wheelchair system confirms that robustness and desired performance of a multivariable system under model uncertainties and unknown external disturbances can indeed be achieved by the combination of triangularization technique and Neuro-Sliding Mode Control. ©2009 IEEE.

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http://hdl.handle.net/10453/11133