Model-based iterative learning control strategies for precise trajectory tracking in gasoline engines

Abstract

In this paper trajectory tracking algorithms for gasoline engines are devised. Specifically, precise reference tracking in engine speed and air-to-fuel ratio is enabled while satisfying initial and final conditions on the center of combustion. Such a tracking of multiple reference trajectories requires a coordinated control action for the air path, the fuel path, and the ignition timing actuators. Combining a dedicated feedforward and feedback controller structure and multivariable model-based norm-optimal parallel iterative learning control strategies, feedforward control trajectories are generated that enable a precise tracking of desired reference trajectories. Experimental results focusing on the termination of the catalyst heating mode show the effectiveness of the proposed methodology, resulting in a control error reduction above 85%.