An Intelligent Control Strategy for Electromagnetically Driven Control Valve Systems Based on DDPG-PID Auto-Tuning

In aerospace, industrial automation, and intelligent equipment, electromagnetically driven valve control systems often suffer from low control accuracy and stalling during switching and servo operations because of strong nonlinearity, parameter uncertainty, frequent operating condition variations, and the empirical nature of conventional PID tuning. To address these issues, this study proposes a PID self-tuning control method based on Deep Deterministic Policy Gradient (DDPG) to improve the dynamic response and robustness of valve control systems. An electromagnetically driven control valve system suitable for both on/off valve modes is constructed, and an interaction framework between the reinforcement learning agent and the Simulink simulation environment is established. The proposed reinforcement-learning control strategy establishes a mapping from the measured system states to the PID gains, enabling the gains to be adaptively adjusted according to the current operating state. To improve training stability and sample efficiency, experience replay and target network soft updates are introduced, and an Actor–Critic architecture is adopted for policy learning and value evaluation. The proposed strategy is further implemented in both the simulation model and MCU controller software, and its performance is compared with traditional PID and fuzzy PID methods. Results show that, in on/off valve mode, the method achieves timed opening, holding, and closing within a preset cycle, while in servo valve mode, it enables accurate position control over a 0~20 mm valve core stroke, with a positioning error of 0.04 mm and a response time below 50 ms. Compared with traditional methods, it shows clear advantages in accuracy, response speed, and adaptability.