The long-distance traditional belt conveyor driven by a single high-power motor has the problems of excessive tension increments and sharp fluctuations in speed and tension. This paper designs a distributed permanent magnet direct drive belt conveyor system. The dynamic model of the conveyor belt unit and the permanent magnet motor is analyzed. The multi-motor ring coupling control strategy and the double sliding film direct torque control strategy of the belt conveyor system are formulated. The mechanical-electrical coupling dynamic model of the belt conveyor system is built. Using MATLAB/Simulink modeling and simulation, the vector control strategy and electromechanical coupling dynamic behavior of the traditional belt conveyor system and the distributed permanent magnet direct drive belt conveyor system under light load start-up and local variable load operation conditions are studied. The results show that: the distributed permanent magnet direct drive belt conveyor system significantly reduces the peak of conveyor belt tension increment; the time spent under the starting light-load operation condition is shorter, and the fluctuation amplitude of speed and tension is smaller; it is possible to reduce the speed and tension fluctuation range of the conveying system and improve the robustness of the conveying system under local variable load conditions. Experiments have verified that increasing the number of drive motors in a conventional belt conveyor can suppress the disturbance caused by local load changes, and the distributed permanent magnet direct-drive belt conveyor has better dynamic regulation performance.

A composite control strategy is proposed to improve the position-tracking performance and anti-interference capabilities of permanent magnet synchronous motors (PMSMs). This strategy integrates an active disturbance rejection controller (ADRC) and a radial basis function neural network (RBFNN) with feedforward control. Initially, the flexibility and robustness of the ADRC are utilized in the position loop control. Subsequently, the parameters of the extended state observer (ESO) within the ADRC are optimized, benefiting from the fast convergence speed and optimal approximation provided by the RBFNN. To further enhance the dynamic tracking performance, a differential feedforward link is introduced between the desired speed and the output signal. The simulation and experimental results demonstrate that when the expected electrical angle inputs are sinusoidal and pulse signals, the incorporation of the feedforward link and the adjustment of parameters in the ADRC lead to improved position-tracking capabilities and greater adaptability to load disturbances.