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Determination of Q-function optimum grid applied on active magnetic bearing control task
Březina, Tomáš ; Krejsa, Jiří
AMB control task can be solved using reinforcement learning based method called Q learning. However there are certain issues remaining to solve, mainly the convergence speed. Two-phase Q learning can be used to speed up the learning process. When table is used as Q function approximation the learning speed and precision of found controllers depend highly on the Q function table grid. The paper is denoted to determination of optimum grid with respect to the properties of controllers found by given method.
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Robotic leg inverse dynamic modelling and design optimization in Simmechanics
Grepl, Robert ; Kratochvíl, Ctirad
This paper deals with the design of inverse dynamical model of pantographic robotic leg in Matlab - SimMechanics. This problem belongs to the class of dynamical inverse modelling, where the positions ofactuators and efector are defined in different coordinate systems. Therefore we also have to use inverse kinematicsmodel. This inverse dynamic model serves as the base of robotic leg design optimization. The definition of valuefunction is based on known properties of drives and variable parameters of the leg design. Optimization maximizesparameters of efector trajectory.
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