基于Policy Gradient的机械臂运动跟踪控制器参数整定

Parameter tuning of manipulator motion tracking controller based on Policy Gradient

针对机械臂运动跟踪控制器的参数自整定问题,设计了一种基于强化学习Policy Gradient法的参数整定器。首先,介绍了机械臂的一种混合动力学模型,根据该系统模型进行了比例微分(proportional-derivative,PD)控制器设计和李雅普诺夫稳定性证明,并由此给出了参数矩阵的范围。其次,设计了基于Policy Gradient的参数整定器,通过引入积分器的方法对其进行改进,使其控制下的参数行为连续化以进一步提高PD控制器的控制效果。最后,以二阶机械臂系统为例进行了仿真验证。实验数据证明了该参数整定器的有效性和可行性,并能有效提升系统的动态性能。

Aiming at the parameter self-tuning problem of the manipulator motion tracking controller, a parameter tuning device based on reinforcement learning Policy Gradient method is designed. Firstly, a hybrid dynamic model of manipulator is introduced. According to the system model, the design of proportional-derivative (PD) controller and the stability of LyapunoVare carried out, and the range of parameter matrix is given. Secondly, the parameter tuning device based on Policy Gradient is designed. The integrator is introduced to improve the parameter tuning of gradient, sOas to further improve the control effect of PD controller. Finally, a second-order manipulator system is taken as an example to verify the effectiveness and feasibility of the parameter tuning device, which effectively improves the dynamic performance of the proposed system.