Active task design in adaptive control of redundant robotic systems

Lu, W; Liu, D

Active task design in adaptive control of redundant robotic systems

Lu, W Liu, D

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Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Robotics and Automation, ACRA, 2017, 2017-December pp. 8 - 15
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Lu, W	en_US
dc.contributor.author	Liu, D https://orcid.org/0000-0002-1581-5582	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Australasian Conference on Robotics and Automation, ACRA, 2017, 2017-December pp. 8 - 15	en_US
dc.identifier.isbn	9781510860117	en_US
dc.identifier.issn	1448-2053	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127887
dc.description.abstract	This paper seeks to use robots' kinematic redundancy to excite the system persistently, through actively designing a secondary task in the null space of a primary task. Resulted convergence of unknown parameters in adaptive control leads to better system stability and performance. A measure in Grassmannian, referred to as Subspace Discrepancy Measure (SDM), is proposed for evaluating the additional benefit from the secondary task in converging unknown parameters to their true values. This measure evaluates the angles among subspaces that the parameter estimations are converging to, given different secondary tasks. The subspaces are obtained from Principal Component Analysis (PCA) on a small amount of samples of parameter estimations. The SDM is used to determine the choice of the secondary task online through a trial-and-evaluation procedure actively. Numerical simulations demonstrated that the secondary task chosen by SDM enhances the parameter convergence.	en_US
dc.relation.ispartof	Australasian Conference on Robotics and Automation, ACRA	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Active task design in adaptive control of redundant robotic systems	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-December	en_US
utslib.for	0913 Mechanical Engineering	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 Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access	*
pubs.declined	2018-09-30T05:27:28.739+1000
pubs.deleted	2018-09-30T05:27:28.739+1000
pubs.publication-status	Published	en_US
pubs.volume	2017-December	en_US

Abstract:

This paper seeks to use robots' kinematic redundancy to excite the system persistently, through actively designing a secondary task in the null space of a primary task. Resulted convergence of unknown parameters in adaptive control leads to better system stability and performance. A measure in Grassmannian, referred to as Subspace Discrepancy Measure (SDM), is proposed for evaluating the additional benefit from the secondary task in converging unknown parameters to their true values. This measure evaluates the angles among subspaces that the parameter estimations are converging to, given different secondary tasks. The subspaces are obtained from Principal Component Analysis (PCA) on a small amount of samples of parameter estimations. The SDM is used to determine the choice of the secondary task online through a trial-and-evaluation procedure actively. Numerical simulations demonstrated that the secondary task chosen by SDM enhances the parameter convergence.

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http://hdl.handle.net/10453/127887