Deep reinforcement learning enhanced PID control for hydraulic servo systems in injection molding machines

To address the issue of insufficient position control accuracy in the servo-hydraulic system of injection molding machines under nonlinear characteristics and external disturbances, this paper proposes a novel adaptive PID control strategy enhanced by the Deep Deterministic Policy Gradient (DDPG) algorithm. An auxiliary servo valve is introduced to improve flow capacity and enhance the system's dynamic response performance. Meanwhile, the DDPG algorithm is utilized to adjust the PID parameters in real time based on tracking errors and system state feedback, thereby improving the controller's adaptability to time-varying operating conditions. Compared with traditional control methods, the proposed strategy demonstrates superior tracking accuracy, faster convergence, and stronger robustness. In particular, this work innovatively integrates the DDPG algorithm with an auxiliary servo valve structure for PID parameter optimization and dynamic performance enhancement, offering new ideas and technical pathways for adaptive control of complex hydraulic systems.