基于RBF神经网络的采煤机姿态预测控制

Predictive Control of Shearer Vertical Steering System Based on RBF Neural Network

针对采煤机姿态自动控制难题,提出了基于RBF神经网络的采煤机姿态预测控制方法。该方法在传统记忆截割的基础上,结合采煤机工作参数,采用RBF神经网络对煤岩特性进行辨识,进一步修正煤岩预测界面,提高其预测精度和可靠性。同时,对滚筒调高系统采用基于RBF神经网络的预测控制方法,在Matlab/Simulink中仿真表明比传统控制方法实时性提高1.5 s。

Considering the auto-control of the vertical steering system in shearer, a novel predictive control scheme based on RBFNN （RBF Neural Network） was presented. The memory cutting system and the working parameters of the shearer were considered in the scheme and RBFNN was used to predict the coal-rock interface. The predictive precision. Then, the predictive control based on RBFNN was used to control the vertical steering system. In Mathub/Simulink simulation shows in real time than tranditional control methods to improve the 1.5 s.