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Servo motor controllers are vital components in industrial applications such as robotics and automated machinery, in which precision, safety, and reliability are essential. This paper presents and evaluates different strategies to leverage Reinforcement Learning (RL) to control servo motors, while at the same time maintaining the traditional cascade control architecture, incorporating filter and feedforward control, as means to ensure understandability and safety of the control structure. First, we implement a multiagent RL architecture, in which the individual components of the cascaded control architecture are assigned a RL agent. Secondly, we implement a single-agent RL architecture, whereas the actor network of a twin-delayed deep deterministic policy gradient (TD3) algorithm is designed to replicate the cascade controller. Initial simulations are followed by experimental validation on a servo motor in a laboratory setting, demonstrating the real-world applicability of the approach. Finally, results are discussed and compared, leading to an assessment of the applicability of the proposed methods in industrial applications.
