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

## TL;DR

This paper introduces a new control strategy for shearer drum height using deep reinforcement learning to improve mining automation and equipment reliability.

## Contribution

A novel DDPG-based self-adaptive height control method for shearers, outperforming traditional and other reinforcement learning approaches.

## Key findings

- The proposed DDPG method achieved a 95.06% accuracy in coal and rock cutting state identification.
- The control system reduced response time to 0.091 s and steady-state error to 0.00052 mm.
- The system showed faster response and stronger anti-interference capability compared to TD3 and SAC algorithms.

## 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 validated through Matlab/Simulink and AMESim co-simulation, as well as a similarity-based physical test platform. Results show that the proposed method significantly outperforms conventional and fuzzy PID controls, reducing response time to 0.091 s and steady-state error to 0.00052 mm. Compared with TD3 and SAC algorithms, the system exhibited faster response, higher stability, and stronger anti-interference capability. The mean maximum error between simulation and experimental results was only 3.14%, confirming the feasibility and robustness of the proposed control strategy. This study provides a reliable approach for intelligent, adaptive height control of shearers under complex coal seam conditions.