Application Research on BP Neural Network PID Control of the Belt Conveyor

Journal of Digital Information Management Volume 9 Number 6 December 2011 ABSTRACT: In order to control reasonably the acceleration and dynamic tension in full-load starting process of conveyor system, accurate design techniques should be adopted. This paper selects the viscoelasticity model suiting to the belt by comparing dynamic performance of some kinds of models, and develops simulation model of driving device with feedback control, which indicating that motor rotation speed and its output torque vary with load and time. For belt conveyors, it has high nonlinear trait, PID controller with fixed parameter can not achieve good performance index. So the PID controller based on neural network is proposed. This design regards belt conveyors as a control object, by analyzing danamic model of belt conveyors, the design of related neural network PID control system based on BP neural network is provided. The BP-ANN is designed with three layers which are input-layer, hidden-layer and output layer in consideration of the control system real time requirement. The results show that the controller based on the neural networks can improve the robustness of the system and has better adaptabilities to the model and environments, compared with the classical PID control, therefore the simulation results are more close to practice.