RESEARCH PAPER
Intelligent Control Method for Continuous Miner Cutting Parameters Based on Strength Constraints
1
School of Mechanical Engineering, Liaoning Technical University, China
A – Conceptualization; B – Methodology; C – Software; D – Validation; E – Formal analysis; F – Investigation; G – Resources; H – Data curation; I – Writing – original draft; J – Writing – review & editing; K – Visualization; L – Supervision; M – Project administration; N – Funding acquisition
Submission date: 2026-03-08
Final revision date: 2026-05-09
Acceptance date: 2026-06-01
Online publication date: 2026-07-15
Corresponding author
KEYWORDS
TOPICS
ABSTRACT
This study addresses the insufficient dynamic strength and low energy efficiency of continuous miners in complex coal seams. A rigid-flexible coupled virtual prototype of the EML340 miner revealed that stress at the cutting arm's hydraulic cylinder connection exceeds the allowable limit (125 MPa). Based on single-factor analysis of cutting parameters and coal-rock hardness, an intelligent Fuzzy Neural Network (FNN) controller was designed to optimize performance under stress constraints. Co-simulation using ADAMS and MATLAB/Simulink demonstrates that the system adaptively adjusts rotational and swing speeds in response to hardness variations. Compared with PID control, the FNN strategy reduces settling time by 87.5%, stress overshoot by 28.6%, and specific cutting energy by 9.3%, ensuring safety while enhancing efficiency. Simulation accuracy was validated via a physical cutting test bench, with a maximum relative error of 4.92% in vibration characteristics. This research provides a technical solution for achieving intelligent, efficient, and safe cutting in continuous miners.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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