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Reinforcement learning control of a single-link flexible robotic manipulator

机译:单链接柔性机器人操纵器的强化学习控制

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摘要

In this study, the authors focus on the reinforcement learning control of a single-link flexible manipulator and attempt to suppress the vibration due to its flexibility and lightweight structure. The assumed mode method and the Lagrange's equation are adopted in modelling to enhance the satisfaction of precision. Two radial basis function neural networks (NNs) are employed in the designed control algorithm, actor NN for generating a policy and critic NN for evaluating the cost-to-go. Rigorous stability of the system has been proven via Lyapunov's direct method. Through Matlab simulation and experiment on the Quanser flexible link platform, the superiority and feasibility of the reinforcement learning control are verified.
机译:在这项研究中,作者专注于单连杆柔性机械手的强化学习控制,并试图抑制由于其柔性和轻巧结构而引起的振动。在建模中采用假设模式方法和拉格朗日方程,以提高精度的满意度。在设计的控制算法中,采用了两个径向基函数神经网络(NN),即参与者NN用于生成策略,评论家NN用于评估成本。系统的严格稳定性已通过Lyapunov的直接方法得到证明。通过在Quanser柔性链接平台上的Matlab仿真和实验,验证了强化学习控制的优越性和可行性。

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