Research on Precise Positioning Sliding Mode Control of Belt Conveyor Unloading Vehicle Based on RBFNN

Abstract In response to the problem of inaccurate positioning and parking of belt conveyor unloading vehicles during operation, based on the analysis of their actual operating scenarios, This study proposes a sliding mode control strategy for belt conveyor unloading vehicles based on Radial Basis Function Neural Network, establish a dynamic model of unloading vehicle based on RBFNN, designs a sliding mode controller based on RBFNN, and builds a physical experimental model of unloading vehicles. The experimental results show that this control strategy can achieve good tracking effect on the speed and displacement of the unloading vehicle, and the accuracy of the unloading vehicle positioning and parking reaches 98% in the end; Compared with no RBFNN, getting closer to the target position in displacement tracking, meeting the accuracy requirements for positioning and parking of belt conveyor unloading vehicles under actual working conditions. It has engineering practicality and provides a new engineering control solution to solve the problems in the field of bulk material conveying.