Fuzzy self-learning control of glass tempering and annealing temperature based on the optimised genetic big data analysis algorithm

The temperature control of glass tempering and annealing process has the problems of the time varying parameters and time lag characteristic. In order to solve this problem, this paper proposes a self-learning fuzzy controller based on improved genetic algorithm and big data analysis. The proposed algorithm can quickly search the global optimal factor by using the big data temperature. Thus the fuzzy control rules are perfected and corrected. The simulation results demonstrate that the proposed control algorithm is suitable for systems with time varying parameters and time lag characteristic.