Simulation on Air Fuel Ratio Control Based on Neural Network

It is a challenge to control the transient air fuel ratio of gasoline engines accurately. In this work, the traditional PI controller was used to control the transient air fuel ratio by using the estimated signal. To verify the validity of the control strategy, a single cylinder gasoline engine model was built with GT (grand touring)-power. Based on this, the simulation model for controlling the air fuel ratio of the gasoline engine was built, using GT-Power/Simulink. The neural network was programmed with S-functions. The simulation results showed a fair self-adaptability of this control strategy, which could effectively avoid enormous calibration experiments that are needed in the transient air fuel ratio control at present.