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.

橡胶堆叠机器人与橡胶块之间产生不可预测的时变粘附力，这使得机器人难以顺利完成橡胶拆卸任务，从而带来了新的机器人控制问题。为解决上述问题，本文提出了一种基于自然梯度行为者（NAC）强化学习的内外环阻抗控制新方法。通过具有时延估计的内/外环阻抗控制来施加所需的阻抗，这可以校正建模误差并补偿非线性动力学项，从而提高系统的计算效率。此外，基于递归最小二乘滤波的NAC强化学习算法用于在线优化阻抗参数，可以提高非结构化动态环境中的阻抗精度和鲁棒性。同时，在分析过程中得出了控制策略的三个稳定性约束。最后，通过搭建实验平台，验证了该控制策略能够使机器人在不可预测的时变粘附力作用下平稳工作，从而减少了振动，提高了橡胶的卸垛性能。