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

Abstract

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