Reinforcement Learning Based Adaptive Control of a Flexible Manipulator

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1723

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DC Field	Value	Language
dc.contributor.author	Subudhi, B D	-
dc.contributor.author	Pradhan, Santanu Kumar	-
dc.date.accessioned	2012-07-05T12:12:59Z	-
dc.date.available	2012-07-05T12:12:59Z	-
dc.date.issued	2010-12	-
dc.identifier.citation	proceedings of the National System Conference (NSC), 2010, NIT Suratkal, Mangalore, 10-12 Dec. 2010	en
dc.identifier.uri	http://hdl.handle.net/2080/1723	-
dc.description	Copyright belongs to proceeding publisher	en
dc.description.abstract	In this paper a new nonlinear adaptive controller using actor-critic based Reinforcement Learning (RL) is proposed to adapt the load pick-up and release operation while following a desired trajectory by the end effector for a Two- Link Flexible Manipulator (TLFM). Simulation results show that the proposed RL based adaptive control gives better trajectory tracking performance and suppression of link vibration compared to conventional adaptive controllers with time varying payload.	en
dc.format.extent	438309 bytes	-
dc.format.mimetype	application/pdf	-
dc.language.iso	en	-
dc.subject	Flexible Manipulator	en
dc.subject	Reinforcement Learning adaptive control	en
dc.subject	Time varying payload	en
dc.title	Reinforcement Learning Based Adaptive Control of a Flexible Manipulator	en
dc.type	Article	en
Appears in Collections:	Conference Papers

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