A novel impedance control method of rubber unstacking robot dealing with unpredictable and time-variable adhesion force

Unpredictable and time-variable adhesion force between the rubber unstacking robot and the rubber block is generated, which makes it difficult for the robot to smoothly complete the rubber disassembly task, thereby bringing about new robot control problems. For solving the above problems, a novel method of inner/outer loop impedance control based on natural gradient actor-critic (NAC) reinforcement learning is proposed in this paper. The required impedance is applied by the inner/outer loop impedance control with time delay estimation, which can correct the modeling error and compensate the nonlinear dynamics term to improve the computational efficiency of the system. In addition, the NAC reinforcement learning algorithm based on recursive least squares filtering is used to optimize the impedance parameters online, which can improve the impedance accuracy and robustness in the unstructured dynamic environment. At the same time, three stability constraints of the control strategy are derived in the analysis process. Finally, by setting up the experimental platform, it is verified that the control strategy can make the robot work smoothly under the action of unpredictable and time-variable adhesion force to reduce vibration and improve rubber unstacking performance.