Adaptive sliding mode control of robotic manipulator based on reinforcement learning

Robotic manipulators usually exhibit time-varying, nonlinear, and coupled dynamics due to the parameter perturbations, disturbances, and other uncertainties. Traditional control algorithms typically do not possess parameters' self-adaptive learning ability, limiting the tracking performance of the robot. In order to address these issues, an adaptive sliding mode control method based on reinforcement learning (ASMRL) is proposed in this paper, where a proportional-integral sliding mode (PISM) controller is used to address the nonlinear problem in the system, and deep deterministic policy gradient (DDPG) agent is adopted to conduct the parameters' learning of the PISM controller using its adaptive characteristics and the autonomous learning ability. The simulation results illustrate that the proposed method can effectively achieve better tracking performance compared with two other control methods, demonstrating the effectiveness of the proposed approach.