Abstract
In a fully mechanized mining face, the coordinated control of coal mining machines has a significant promoting effect to perfect the mining environment and improve the efficiency of coal production and has become a research focus all over the world. In this paper, a cooperative control method based on the integration of fuzzy logic theory and neural networks was proposed. The improved Elman neural network (ENN) through a threshold strategy was presented to predict the running parameters of coal mining machines. On the basis of coupling analysis of coal mining machines, the expert knowledge base of scraper conveyor was established based on fuzzy logic theory. Furthermore, the probabilistic neural network (PNN) was applied to evaluate the running status of scraper conveyor, and the cooperative control flow was designed and analyzed. Finally, a simulation example was provided and the comparison results illustrated that the proposed method was feasible and superior to the manual control.
Highlights
Industrial process control has been developed for many years and with the improvement of productivity, the complexity and uncertainty of system control are becoming more and more serious
In order to adapt to the changes in control tasks and goals, system control methods are developed from the classical feedback control theory to the intelligent control theory based on neural networks [1]
In order to demonstrate the superiority of proposed cooperative control method to manual operation, a comparison and analysis are presented in this paper
Summary
Industrial process control has been developed for many years and with the improvement of productivity, the complexity and uncertainty of system control are becoming more and more serious. As a special multiple equipment coordinated system, the control of coal mining machines in fully mechanized mining face has become a research focus to improve the control effect and coal mining efficiency. It is necessary to study the cooperative control theory and method for fully mechanized mining machines; the field operators can be reduced and the safe and efficient production in fully mechanized mining face can be improved. A coordinated control method based on fuzzy logic theory and neural networks is proposed to adapt to the complex, dynamic, and uncertain control requirements of the fully mechanized coal mining face [4, 5]. The improved ENN is proposed to predict the running parameters of coal mining machines, and the fuzzy logic theory is presented to establish the expert knowledge base for scraper conveyor, which provides. The simulation results indicate that the proposed method can improve the mining efficiency, reduce the energy consumption, and may provide the foundation for the unmanned coal mining face
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