Research on robot floating polishing actuator based on deep reinforcement learning algorithm
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摘要: 为实现机器人恒力打磨的需求，文章设计了浮动打磨执行器，进行了打磨控制算法研究和浮动打磨执行器的结构设计，并对浮动打磨执行器系统进行受力分析和动力学建模。在传统PID控制算法的基础上，采用DDPG深度强化学习算法进行PID控制参数的整定，并开展浮动打磨执行器恒力性能实验验证。实验结果表明，文章设计的浮动打磨执行器能够满足恒力控制的要求。通过DDPG深度强化学习算法对PID控制参数整定，减少了繁琐的调参步骤，且具有更好的恒力控制性能。Abstract: To meet the constant force polishing needs of robots, this paper designs a floating polishing actuator and conducts research on polishing control algorithms. The structural design of the floating polishing actuator was carried out, and the force analysis and dynamic modeling of the floating polishing actuator system were carried out. On the basis of traditional PID control algorithms, the DDPG deep reinforcement learning algorithm is used to tune the PID control parameters. Conduct experimental verification of the constant force performance of the floating polishing actuator, and the experimental results show that the floating polishing actuator designed in this paper can meet the requirements of constant force control. By using the DDPG deep reinforcement learning algorithm to tune PID control parameters, the tedious parameter tuning steps are reduced, and it has better constant force control performance.
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Key words:
- floating grinding /
- constant force control /
- deep reinforcement learning
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表 1 输出力性能指标比较
性能指标 PID参数控制 DDPG参数整定 力偏差均值/N －0.81 －0.049 力偏差均方差值/N 0.727 0.468 上升时间/s 0.06 0.02 
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