Data-driven model predictive control of Air-fuel Ratio for PFISI engine

Air-fuel Ratio (AFR) control is considered as one of the most important issues in engine control. In this paper, a data-driven model predictive controller is designed for AFR control of Port Fuel Injection Spark Ignition (PFISI) gasoline engine system. According to the input-output data of a engine simulation model provided by the commercial software enDYNA, the future dynamic of engine system can be predicted. Furthermore, based on the model predictive control (MPC) approach, the control requirement is converted to the optimal control objective, then the control action is obtained by solving the optimal problem. Finally, the simulation results show the effectiveness of the proposed controller.

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