Predictive End-Effector Control of Manipulators on Moving Platforms under Disturbance

Woolfrey, J; Lu, W; Liu, D

Predictive End-Effector Control of Manipulators on Moving Platforms under Disturbance

Woolfrey, J

Lu, W Liu, D

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Robotics, 2021, 37, (6), pp. 2210-2217
Issue Date:: 2021-12-01

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Field	Value	Language
dc.contributor.author	Woolfrey, J https://orcid.org/0000-0001-5926-5669
dc.contributor.author	Lu, W
dc.contributor.author	Liu, D https://orcid.org/0000-0002-1581-5582
dc.date.accessioned	2022-01-04T01:00:43Z
dc.date.available	2022-01-04T01:00:43Z
dc.date.issued	2021-12-01
dc.identifier.citation	IEEE Transactions on Robotics, 2021, 37, (6), pp. 2210-2217
dc.identifier.issn	1552-3098
dc.identifier.issn	1941-0468
dc.identifier.uri	http://hdl.handle.net/10453/152629
dc.description.abstract	This article proposes a predictive end-effector control method for manipulators operating on mobile platforms subjected to unwanted base motion. Time series is used to forecast the base motion using historical state information. Then, a trajectory specified in the inertial frame is transformed to a predicted trajectory with respect to the manipulator. By tracking this transformed trajectory, the manipulator negates the base motion. A model-predictive control problem is formulated via quadratic programming (QP) to track said trajectory over the prediction horizon. Only the first control action in the control sequence is constrained by kinematic feasibility. In this manner, QP can be swiftly solved with linear inequality constraints. It is shown that the actual joint trajectory executed by the manipulator is always kinematically feasible. Moreover, tracking error can still be reduced despite future predicted control actions being infeasible. The method is validated through both simulation and experiment. The proposed method can reduce pose error by over 60% compared to a proportional-integral feedback controller.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/LP150100935
dc.relation.ispartof	IEEE Transactions on Robotics
dc.relation.isbasedon	10.1109/TRO.2021.3072544
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	Predictive End-Effector Control of Manipulators on Moving Platforms under Disturbance
dc.type	Journal Article
utslib.citation.volume	37
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
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/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-01-04T01:00:42Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	37
utslib.citation.issue	6

Abstract:

This article proposes a predictive end-effector control method for manipulators operating on mobile platforms subjected to unwanted base motion. Time series is used to forecast the base motion using historical state information. Then, a trajectory specified in the inertial frame is transformed to a predicted trajectory with respect to the manipulator. By tracking this transformed trajectory, the manipulator negates the base motion. A model-predictive control problem is formulated via quadratic programming (QP) to track said trajectory over the prediction horizon. Only the first control action in the control sequence is constrained by kinematic feasibility. In this manner, QP can be swiftly solved with linear inequality constraints. It is shown that the actual joint trajectory executed by the manipulator is always kinematically feasible. Moreover, tracking error can still be reduced despite future predicted control actions being infeasible. The method is validated through both simulation and experiment. The proposed method can reduce pose error by over 60% compared to a proportional-integral feedback controller.

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http://hdl.handle.net/10453/152629