Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1722
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dc.contributor.authorSubudhi, B D-
dc.contributor.authorPradhan, Santanu Kumar-
dc.date.accessioned2012-07-05T12:08:21Z-
dc.date.available2012-07-05T12:08:21Z-
dc.date.issued2010-12-
dc.identifier.citationproceedings of the IEEE sponsored International Conference on Industrial Electronics, Control & Robotics (IECR), 2010 ,NIT Rourkela, Rourkela , pp.129-136, 27-29 Dec. 2010en
dc.identifier.urihttp://hdl.handle.net/2080/1722-
dc.descriptionCopyright belongs to proceeding publisheren
dc.description.abstractThis paper proposes a new adaptive control using the concept of reinforcement learning to address adaptivity for varied payload conditions for a two-link flexible manipulator (TLFM). The application of reinforcement learning has been implemented using a method called adaptive dynamic programming. Decentralized controllers for the decoupled system have been also designed using LQR technique. Then the reinforcement learning is used to tune the gains of the optimal control to adapt in terms of different payload to the manipulator end effecter. Simulation results show that proposed controller provides better end point tracking then LQR fixed gain controller.en
dc.format.extent1006972 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.subjectFlexible-Link Manipulatoren
dc.subjectActor Critic baseden
dc.subjectAdaptive dynamic programming Variable Tip Massen
dc.titleDirect Adaptive Control of a Flexible Robot using Reinforcement Learningen
dc.typeArticleen
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