This paper proposes a deep reinforcement learning (deep RL) method for simultaneously designing several notch filters in complex industrial servo systems. Notch filters are highly effective for suppressing resonances in motion control systems and are widely utilized in industry. However, severe limitations exist in complex servo systems because there are many vibration modes that are difficult to identify. In such cases, several notch filters must be used, but the task of tuning these filters involves lengthy empirical procedures by well-experienced engineers. To automate this tuning process, this paper proposes a novel design method that can design several notch filters simultaneously for the first time. In this method, a deep deterministic policy gradient (DDPG) algorithm with a vector stability margin as the reward function is utilized to find filter parameters in the frequency domain. The proposed method simultaneously finds a set of many parameters for several notch filters that are optimal with respect to stability. Using a real industrial servo system that has multiple resonances, it is demonstrated that the proposed method effectively finds the optimal parameters for several notch filters and successfully suppresses multiple resonances to provide desired performances.

中文翻译：

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基于深度强化学习的复杂工业伺服系统陷波滤波器设计方法

本文提出了一种深度强化学习（deep RL）方法，用于在复杂的工业伺服系统中同时设计多个陷波滤波器。陷波滤波器对于抑制运动控制系统中的共振非常有效，并在工业中得到广泛应用。然而，复杂的伺服系统存在严重的局限性，因为存在许多难以识别的振动模式。在这种情况下，必须使用多个陷波滤波器，但调整这些滤波器的任务涉及由经验丰富的工程师进行的冗长的经验程序。为了自动化这个调谐过程，本文首次提出了一种新颖的设计方法，可以同时设计多个陷波滤波器。在这种方法中，深度确定性策略梯度 (DDPG) 算法具有向量稳定裕度作为奖励函数，用于在频域中查找滤波器参数。所提出的方法同时为几个在稳定性方面最优的陷波滤波器找到一组许多参数。使用具有多个共振的真实工业伺服系统，证明了所提出的方法有效地找到了多个陷波滤波器的最佳参数并成功地抑制了多个共振以提供所需的性能。