Research on the mill feeding system of an elastic variable universe fuzzy control based on particle swarm optimization algorithm

The grinding process in the concentrator is a part of the largest energy consumption, but also the most likely to cause a waste of resources, so the optimization of the grinding process is a very important link. The traditional fuzzy controller relies solely on the expert knowledge summary to construct control rules, which can cause significant steady-state errors in the model. In order to solve the above problem, this paper proposes an elastic variable universe fuzzy control based on Particle Swarm Optimization (PSO) algorithm. The elastic universe fuzzy control model does not need precise fuzzy rules, but only needs to input the general trend of the rules, and the division of the universe is performed by the contraction-expansion factor. The control performance is directly related to the contraction-expansion factor, so this article also proposes using particle swarm optimization to optimize the scaling factor to achieve the optimal value. Finally, simulation models of traditional fuzzy control and elastic universe fuzzy control of feeding system of mill were built using Python to verify the control effect. Its simulation results show that the time of the reaction of the fuzzy control system in the elastic variable theory universe based on particle swarm optimization was shorter by 34.48% comparing to the traditional one. Elastic variable universe fuzzy control based on particle swarm optimization (PSO) effectively improved the control accuracy of the mill feeding system and improved the response speed of the system to a certain extent.