Research on self-adaptive height adjustment control of shearer based on deep deterministic policy gradient.

ABSTRACT: As a core component of the fully mechanized mining face, intelligent control of the shearer is fundamental to achieving unmanned mining and improving equipment reliability. To address the limitations of traditional optimization and deep reinforcement learning algorithms in achieving rapid and accurate self-adaptive control, this study proposes a novel shearer drum height control strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The 4602 workface at Yangcun Coal Mine and the MG2 × 55/250-BWD shearer model were used as engineering cases. A hybrid SVD-CWT and AlexNet transfer learning method was employed to identify coal and rock cutting states, achieving an accuracy of 95.06%. A DDPG-based self-adaptive hydraulic height adjustment model was then developed and validat

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12826524 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature