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Application of the Reinforcement Learning method for adaptive electric drive control with variable parameters

机译:加固学习方法在具有可变参数的自适应电驱动控制中的应用

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In this work an artificial neural network was used, which learned using Reinforcement Learning algorithm to control a non-stationary object with a complex mechanical structure that depend on the angular position of the shaft. Critic is presented as a function of control error and control cost, which ensures stability of the system in a long-term performance, without the need to disable the adaptation algorithm. Simulation and experimental results are presented.
机译:在这项工作中,使用了一种人工神经网络,其使用加强学习算法来控制具有复杂机械结构的非静止物体,这取决于轴的角位置。 批评者作为控制误差和控制成本呈现,可确保系统的稳定性在长期性能中,而无需禁用适应算法。 提出了模拟和实验结果。

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