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dc.contributor.authorSong, KTen_US
dc.contributor.authorChu, TSen_US
dc.date.accessioned2019-04-02T05:59:25Z-
dc.date.available2019-04-02T05:59:25Z-
dc.date.issued1998-01-01en_US
dc.identifier.issn0967-0661en_US
dc.identifier.urihttp://dx.doi.org/10.1016/S0967-0661(97)10058-2en_US
dc.identifier.urihttp://hdl.handle.net/11536/150182-
dc.description.abstractThis paper presents a learning control design, together with an experimental study for implementing it on an industrial robot working in constrained environments. A new reinforcement learning scheme is proposed, to enable performance optimization in industrial robots. Using this scheme, the learning process is split into generalized and specialized learning phases, increasing the convergence speed and aiding practical implementation Initial computer simulations were carried out for force tracking control of a two-link robot arm. The results confirmed that even without calculating the inverse kinematics or possessing the relevant environmental information, operating rules for simultaneously controlling the force and velocity of the robot arm can be achieved via repetitive exploration. Furthermore, practical experiments were carried out on an ABB IRB-2000 industrial robot to demonstrate the developed reinforcement-learning scheme for real-world applications. Experimental results verify that the proposed learning algorithm can cope with variations in the contact environment, and achieve performance improvements. (C) 1998 Elsevier Science Ltd. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectlearning controlen_US
dc.subjectstochastic reinforcement learningen_US
dc.subjectindustrial robotsen_US
dc.subjectforce tracking controlen_US
dc.subjectperformance optimizationen_US
dc.titleReinforcement learning and its application to force control of an industrial roboten_US
dc.typeArticleen_US
dc.identifier.doi10.1016/S0967-0661(97)10058-2en_US
dc.identifier.journalCONTROL ENGINEERING PRACTICEen_US
dc.citation.volume6en_US
dc.citation.spage37en_US
dc.citation.epage44en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000073957600004en_US
dc.citation.woscount8en_US
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