Adaptive Proportional Integral Robust Control of an Uncertain Robotic Manipulator Based on Deep Deterministic Policy Gradient

An adaptive proportional integral robust (PIR) control method based on deep deterministic policy gradient (DDPGPIR) is proposed for n-link robotic manipulator systems with model uncertainty and time-varying external disturbances. In this paper, the of nonlinear dynamic model, disturbance, friction resistance are integrated into system, term used to compensate system. addition, information as input DDPG agent search optimal parameters controller in continuous action space. To ensure agent’s stable efficient learning, a reward function combining Gaussian Euclidean distance designed. Finally, taking two-link robot an example, simulation experiments DDPGPIR other methods compared. The results show that has better ability, robustness, higher trajectory tracking accuracy.