Abstract: At present, the local ventilator control mainly uses traditional PID controllers. The parameters of the controller are fixed and selected based on experience. It is difficult to achieve the optimal combination of membership functions and fuzzy rules, which makes it difficult for the controller to meet the adaptive control requirements of local ventilator. In order to solve this problem, a fuzzy PID controller for mine local ventilator optimized by improved genetic algorithm is designed. In the improved genetic algorithm, the Euclidean distance is introduced to increase the diversity of the population, and the adaptive crossover and mutation probability are introduced to improve the convergence of the algorithm. In the encoding process, the proportional fraction is used to indirectly optimize the membership functions, which enables the improved genetic algorithm to optimize both the membership functions and fuzzy rules. The optimized membership functions and fuzzy rules are imported into the fuzzy PID controller. The controller can adaptively adjust the air volume of the local ventilator through the inverter according to the different working status of the local ventilator so as to realize the dynamic adjustment of the local ventilator. The simulation results show that compared with the traditional fuzzy PID controller, the improved fuzzy PID controller can basically achieve no overshoot, the rise time is shortened by 56.25%, and the stabilization time is shortened by 47.06%, which can better meet the control requirements of mine local ventilator.