Human-behavior learning for infinite-horizon optimal tracking problems of robot manipulators

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dc.contributor.authorPerrusquía, Adolfo
dc.contributor.authorYu, Wen
dc.date.accessioned2022-02-07T16:27:24Z
dc.date.available2022-02-07T16:27:24Z
dc.date.issued2022-02-01
dc.description.abstractIn this paper, a human-behavior learning approach for optimal tracking control of robot manipulators is proposed. The approach is a generalization of the reinforcement learning control problem which merges the capabilities of different intelligent and control techniques in order to solve the tracking task. Three cognitive models are used: robot and reference dynamics and neural networks. The convergence of the algorithm is achieved under a persistent exciting and experience replay fulfillment. The algorithm learns online the optimal decision making controller according to the proposed cognitive models. Simulations were carry out to verify the approach using a 2-DOF planar robot.en_UK
dc.identifier.citationPerrusquía A, Yu W. (2022) Human-behavior learning for infinite-horizon optimal tracking problems of robot manipulators. In: 2021 60th IEEE Conference on Decision and Control (CDC), 14-17 December 2021, Austin, Texas, USAen_UK
dc.identifier.eisbn978-1-6654-3659-5
dc.identifier.isbn978-1-6654-3660-1
dc.identifier.issn2576-2370
dc.identifier.urihttps://doi.org/10.1109/CDC45484.2021.9683719
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17551
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectHeuristic algorithmsen_UK
dc.subjectNeural networksen_UK
dc.subjectDecision makingen_UK
dc.subjectReinforcement learningen_UK
dc.subjectMathematical modelsen_UK
dc.subjectTrajectoryen_UK
dc.subjectNonlinear dynamical systemsen_UK
dc.titleHuman-behavior learning for infinite-horizon optimal tracking problems of robot manipulatorsen_UK
dc.typeConference paperen_UK

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