Reinforcement learning-based adaptive control of shearer traction speed under complex load conditions

Precise regulation of shearer traction speed is critical for efficient coal cutting and stable system operation in underground mining. However, complex coal–rock characteristics and highly variable working conditions pose significant challenges for experimental validation and control design. To address these issues, a co-simulation platform integrating EDEM, RecurDyn, and Simulink is developed, enabling high-fidelity modeling of shearer–coal wall interactions under varying coal hardness and structural heterogeneity. Mechanical parameters of the coal-rock are calibrated through uniaxial compression tests to ensure the reliability of key indicators, including traction resistance, drum load, and energy consumption. Based on this framework, an adaptive PID control strategy incorporating Deep Deterministic Policy Gradient (DDPG) is designed to improve the accuracy and robustness of traction speed regulation. Simulation results show that the proposed DDPG-PID controller achieves mean absolute errors of 1.27 r/min and 1.77 r/min under two typical variable load conditions, outperforming the conventional PID and PPO-PID controllers in speed tracking accuracy. These results demonstrate that the proposed approach effectively reduces speed tracking errors under diverse operating conditions, providing a reliable methodology for intelligent traction control of shearers in complex underground environments.