Adaptive Cutting Control for Roadheaders Based on Performance Optimization

Liu, Qingyun; Lu, Chao; Liu, Tao; Xu, Zhangbao

doi:10.3390/machines9030046

Cited by 8 publications

(6 citation statements)

References 29 publications

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“…Li et al 14,15 Li combines BP neural network with PID control method, through the feedback adjustment method of cutting load and motor current, he realizes the adaptive control of the yaw speed of the roadheader and improves the stability of the roadheader. Liu et al 16 proposed an adaptive variable speed cutting control method based on cutting performance optimization by analyzing the working principle of the roadheader, which realized adaptive variable speed cutting in the tunneling process. However, the above research did not carry out theoretical analysis on chatter phenomenon during cutting.…”

Section: Prediction Methods Of Cutting Stability Of Roadheader Based ...mentioning

confidence: 99%

Prediction method of cutting stability of roadheader based on the Newton–Lagrange mixed discrete method

Zhu

Xie

Wang

2022

Sci Rep

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In order to study the influence of the multiple interaction of coal and rock and the velocity effect of the cutting head on the flutter stability of the cantilever roadheader, considering the physical characteristics of coal and rock, the structural parameters of the cutting head and the motion parameters of the cutting head, the functional expression of the cutting depth of the cutting head and the participating cutting teeth is fitted, the cutting state mechanical characteristic equation of the dynamic cutting of the cutting teeth is established, and based on the mapping relationship between the cutting teeth and the cutting head, a cutting dynamic model including multiple interactions between cutting head and coal and rock and the speed effect is constructed. An improved discrete method based on Newton–Lagrange mixed interpolation is proposed, and the influence law of the coupling effect of regeneration effect and velocity effect on the stability of cutting flutter under cutting state is clarified. The improved full discrete method is compared with the full discrete method and the semi-discrete method, and the superiority of the improved full discrete method based on the mixed interpolation method is proved. Based on the improved total discrete method, the influence of different cutting system dynamic parameters on the stability is studied. A cutting head coal rock system is built to simulate the flutter of the cutting system. The results show that the improved fully discrete method can reasonably predict the actual cutting state.

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Section: Prediction Methods Of Cutting Stability Of Roadheader Based ...mentioning

confidence: 99%

Prediction method of cutting stability of roadheader based on the Newton–Lagrange mixed discrete method

Zhu

Xie

Wang

2022

Sci Rep

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show abstract

“…30 The Hilbert Huang transform has multi-resolution and stronger local characteristics, but there is mode aliasing. 31 Wavelet analysis is a kind of spectrum analysis technology with adjustable window, but it cannot have high time resolution and high frequency resolution. 32 As an extension of wavelet, wavelet packet can decompose the low frequency and high frequency parts of the signal at the same time to improve the time-frequency resolution.…”

Section: Signal Feature Extractionmentioning

confidence: 99%

Rock hardness identification based on optimized PNN and multi-source data fusion

Tian

Song

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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To solve the problem that it is difficult to identify the cutting rock wall hardness of the roadheader in coal mine, a recognition method of cutting rock wall hardness is proposed based on multi-source data fusion and optimized probabilistic neural network. In this method, all kinds of cutting signals (the vibration signal of cutting arm, the pressure signal of hydraulic cylinders and current signal of cutting motor) are analyzed by wavelet packet to extract the feature vector, and the multi feature signal sample database of rock cutting with different hardness is established. To solve the problems of uncertain spread and complex network structure of probabilistic neural network (PNN), a PNN optimization method based on differential evolution algorithm (DE) and QR decomposition was proposed, and the rock hardness was identified based on multi-source data fusion by optimizing PNN. Then, based on the ground test monitoring data of a heavy longitudinal roadheader, the method is applied to recognize the cutting rock hardness, and compared with other common pattern recognition methods. The experimental results show that the cutting rock hardness recognition based on multi-source data fusion and optimized PNN has higher recognition accuracy, and the overall recognition error is reduced to 6.8%. The recognition of random cutting rock hardness is highly close to the actual. The method provides theoretical basis and technical premise for realizing automatic and intelligent cutting of heading face.

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“…In order to improve the cutting performance under different coal hardness values, the fluctuation coefficient of the cutting head load

, and

and the specific energy consumption H W were used as optimization targets, the swing ( v ) and rotation ( n ) speeds were selected as the optimized variables, and the cutting motor power and speed range were used as constraints. For optimization methods and parameters, please refer to the literature [ 40 ], and the optimization results are shown in Table 5 and Table 6 .…”

Section: Control and Simulationmentioning

confidence: 99%

Coal and Rock Hardness Identification Based on EEMD and Multi-Scale Permutation Entropy

Liu

et al. 2021

Entropy

Self Cite

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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.

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Adaptive Cutting Control for Roadheaders Based on Performance Optimization

Cited by 8 publications

References 29 publications

Prediction method of cutting stability of roadheader based on the Newton–Lagrange mixed discrete method

Prediction method of cutting stability of roadheader based on the Newton–Lagrange mixed discrete method

Rock hardness identification based on optimized PNN and multi-source data fusion

Coal and Rock Hardness Identification Based on EEMD and Multi-Scale Permutation Entropy

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