In this article, a deep reinforcement learning-based strategy for the optimization of gain parameters of a cross-coupled controller is proposed. First, to compensate the contour error caused by servo lag, a proportional–integral (PI)-type cross-coupled controller is designed, with its gain parameters being determined using the cut-off frequency of the contour error transfer function. Next, on the basis of a deep reinforcement learning algorithm, a neural network structure suitable for contour error compensation is established, through which the optimal gain parameters are obtained. Finally, some experiments are carried out, in which the optimal gain parameters sought through off-line learning schemes are applied to the biaxial motion control. The results indicate that the proposed gain parameters optimization strategy can effectively converge the gain parameters with the optimal intervals, and that the optimal gain parameters obtained by the proposed strategy can significantly improve the contour control accuracy in biaxial contour tracking tasks.

在本文中，提出了一种基于深度强化学习的交叉耦合控制器增益参数优化策略。首先，为了补偿伺服滞后引起的轮廓误差，设计了比例积分（PI）型交叉耦合控制器，其增益参数由轮廓误差传递函数的截止频率确定。接下来，在深度强化学习算法的基础上，建立适合轮廓误差补偿的神经网络结构，通过该结构获得最优增益参数。最后，进行了一些实验，将通过离线学习方案寻求的最佳增益参数应用于双轴运动控制。