Rei Chosei, Wang Qichao, Chen Linlin, Yan Xinhua, Zhang Peng, Fu Liwei, Wang Chong, Liu Xinghui
Nobot Intelligent Equipment (Shandong) Co., Ltd, Liaocheng, Shandong, China.
School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng, Shandong, China.
Sci Rep. 2024 Jun 19;14(1):14127. doi: 10.1038/s41598-024-63384-2.
Since conventional PID (Proportional-Integral-Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC (Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm's feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability.
由于传统的比例-积分-微分（PID）控制器在不断变化的环境条件下几乎无法控制机器人实现恒力磨削的稳定，因此有必要在传统PID控制器中添加一个补偿项。提出了一种基于软演员-评论家（SAC）的最优参数寻优算法，以解决补偿项参数难以获取的问题，包括训练状态动作和归一化预处理、奖励函数设计以及目标深度神经网络设计。该算法用于寻找最优控制器补偿项参数，并应用于PID控制器，通过机器人的逆运动学完成补偿，以实现恒力磨削控制。为验证该算法的可行性，建立了具有力敏信息的磨削机器人仿真模型，仿真结果表明，用该算法训练的控制器能够实现机器人的恒力磨削。最后，搭建了机器人恒力磨削实验系统平台进行测试，验证了最优参数寻优算法对机器人恒力磨削的控制效果，且具有特定的环境适应性。
