Abstract
A multiparameter control strategy and method for the cutting arm of a roadheader is proposed through the operation analysis of roadheader. The method can address the problems of low intelligence and low cutting efficiency faced by the roadheader in the cutting process. The control strategy is divided into two parts: the cutting load identification part and the swing speed control part. The former part is designed using a backpropagation neural network that is optimized by an improved particle swarm optimization algorithm. The latter part is optimally designed using a fuzzy PID controller with improved simulated annealing particle swarm optimization. The simulation analysis in SIMULINK showed that the response time was reduced, proving the robustness of the method. In addition, experimental studies verified the good control effect of the method under different cutting states. The proposed method uses multiparameter to intelligently change the swing speed, providing a theoretical and practical basis for the realization of intelligent and unmanned cutting of roadheader.
Highlights
IntroductionWith the development of coal mining technology and the increasingly strict safety requirements, the intelligentization of coal mining equipment has become an inevitable trend
With the development of coal mining technology and the increasingly strict safety requirements, the intelligentization of coal mining equipment has become an inevitable trend.e intelligent control of roadheader, which is one of the most important equipment for underground excavation work in coal mines, especially for the roadway drivage, has always been a research topic of interest in the field of coal science [1,2,3]
According to Jasiulek and Swider [26], an artificial neural network based on various parameters was used to identify the cutting load to obtain a suitable angular speed for the cutting arm swing
Summary
With the development of coal mining technology and the increasingly strict safety requirements, the intelligentization of coal mining equipment has become an inevitable trend. Li and Liu [19] proposed a fuzzy control method based on GAs optimization, in which the cutting motor current is used as a feedback signal to change the cutting arm swing speed. According to Jasiulek and Swider [26], an artificial neural network based on various parameters was used to identify the cutting load to obtain a suitable angular speed for the cutting arm swing. In response to the nonlinear and complex dynamic behavior of the cutting process in roadheader, the main control methods for the cutter arm swing speed include self-adaptive control [30], robust control [31], sliding-mode control [32], and neural network control [33]. (3) an experiment has been designed to verify the advantages of the control method in speed, stability, and robustness
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