Temperature Control Algorithm of Underground Air Conditioning in Station

Abstract

Aiming at the air-conditioning temperature control of underground station, the air-conditioning temperature controller of underground station is designed based on proportional-integral-differential neural network, the bat algorithm is used to select the initial weights, the radial basis function neural network prediction model is used to optimize the design of the predictor, and the designed control system is simulated on the MATLAB simulation platform. The simulation results show that the dynamic characteristics and steady-state characteristics of the PID controller optimized by bat algorithm are better than those of the PID controller tuned by conventional methods. After optimization by radial basis function neural network prediction model, the rapidity and stability of controller are effectively improved.