Perrusquía, AdolfoYu, Wen2022-02-072022-02-072022-02-01Perrusquía 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, USA978-1-6654-3660-12576-2370https://doi.org/10.1109/CDC45484.2021.9683719https://dspace.lib.cranfield.ac.uk/handle/1826/17551In 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.enAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/Heuristic algorithmsNeural networksDecision makingReinforcement learningMathematical modelsTrajectoryNonlinear dynamical systemsConference paper978-1-6654-3659-5