An effective PID control method of air conditioning system for electric drive workshop based on IBK-IFNN two-stage optimization

A proportional-integral-derivative (PID) controller is a commonly used method for controlling air conditioning systems in electric drive workshops. However, traditional PID controllers have several drawbacks, such as poor control performances, weak adaptive abilities, and bad anti-interference capabilities, which render them unsuitable for the strict environmental requirements of electric drive workshops. Therefore, to compensate for the above deficiencies, first, this study presents a two-stage PID optimization control method, which includes optimizing the fuzzy neural network in the first stage and optimizing the PID controller parameters in the second stage. After that, a two-stage optimization algorithm based on an improved black-winged kite with an improved fuzzy neural network (IBK-IFNN) is designed to adapt the proposed control model. Finally, co-simulation experiments and applications are conducted in the electric drive workshop of an automobile manufacturer to validate the effectiveness of the proposed method. The results demonstrate that the proposed method not only improves the convergence speed and search capability of the IBK-FNN algorithm but also outperforms other controllers in terms of its control performance, adaptive ability, anti-interference capability, and comprehensive score.