This study offers an efficient hardness identification approach to address the problem of poor real-time performance and accuracy in coal and rock hardness detection. To begin, Ensemble Empirical Mode Decomposition (EEMD) was performed on the current signal of the cutting motor to obtain a number of Intrinsic Mode Functions (IMFs). Further, the target signal was selected among the IMFs to reconstruct the current signal according to the energy density and correlation coefficient criteria. After that, the Multi-scale Permutation Entropy (MPE) of the reconstructed signal was trained by the Adaboost improved Back Propagation (BP) neural network, in order to establish the hardness recognition model. Finally, the cutting arm’s swing speed and the cutting head’s rotation speed were adjusted based on the coal and rock hardness. The simulation results indicated that using the energy density and correlation criterion to reconstruct the signal can successfully filter out noise interference. Compared to the BP model, the relative root-mean-square error of the Adaboost-BP model decreased by 0.0633, and the prediction results were more accurate. Additionally, the speed control strategy based on coal and rock hardness can ensure the efficient cutting of the roadheader.

Aiming at addressing the problems of high specific energy consumption for cutting and slow response to the change of hardness in the control of existing mining roadheaders, an adaptive variable speed cutting control method based on cutting performance optimization is proposed by analyzing the working principle of roadheaders. Firstly, cylinder pressure and motor current are invoked as the criteria to judge load changes. Particle swarm optimization is utilized to optimize the cutting parameters under different impedance. Then, the relation between cutting speed, motor current and cylinder pressure is established by using fuzzy neural network to train cutting parameters and identification parameters under different conditions. Finally, the vector control of motor and electro-hydraulic servo valve is used to control the cutting speed. The results show that the cutting unit can adapt to different load signals and always keep the roadheader in the optimal working state. The rotation speed regulation of the cutting head reaches the stable state after 0.05 s, with the overshoot of 1.42%. The swing speed regulation of the cutting head reaches the stable state after 1 s, with the overshoot of 5.3%. Conclusions provide a basis for improving the cutting efficiency and prolonging the working life of the roadheader.