Predictive Control of Belt Conveyor Based on Recursive Fuzzy Neural Network

Belt conveyors play an important role in the dry bulk material handing process. This paper presents an innovative approach to intelligent tracking and speed control for belt conveyors. The proposed method utilizes a recursive fuzzy neural network for predictive control, which is particularly effective for managing nonlinear systems and unexpected events during operation. By establishing a sparse second-order matrix model derived from the dynamic model of the belt conveyor, a recursive neural network control is designed to achieve speed tracking. To validate the effectiveness of the proposed method, simulation experiments are conducted to optimize material flow rate and speed under real working conditions. The results demonstrate a remarkable 18.4% reduction in energy consumption, highlighting the practical benefits of this approach.