Design of Adaptive Control System for Intake Air Volume of Piston Compressor Based on Fuzzy Neural Network PID Controller

Piston compressors are widely used in chemical industry and other fields. At present, the market puts forward a working condition requirement for piston compressor to adapt to the change of intake air volume. However, the classical PID control has shortcomings in stability time, anti-interference and PID parameter online tuning, which does not meet the requirements of working conditions. Therefore, this paper designs the adaptive control system of the intake air volume of the piston compressor according to the variable speed air volume regulation strategy and establishes a mathematical model. Using BP neural network and fuzzy control theory, a fuzzy neural network PID controller is built. Using Matlab/Simulink, a step response simulation experiment is carried out. Using C# language, the upper computer of the system is programmed. The simulation results show that the stability time of the intake air volume adaptive control system based on the fuzzy neural network PID controller is 1.63 seconds, which is 0.55 seconds and 1.6 seconds faster than that of the intake air volume adaptive control system based on the adaptive fuzzy PID controller and the classical PID controller respectively, and its anti-interference ability is stronger, which is more suitable for the realization of the intake air volume adaptive control system controller under this working condition.