Model-based iterative learning control strategies for precise trajectory tracking in gasoline engines. (June 2019)

• Record Type: Journal Article
• Authors: Hedinger, R.; Zsiga, N.; Salazar, M.; Onder, C.
• Is Part Of: Control engineering practice. Volume 87(2019)
• Page Start: 17
• Page End: 25
• Publication Date: 2019-06
• Subjects: Catalyst heating mode -- Iterative learning control, ILC -- Linear parameter-varying system, LPV -- Linear time-varying system, LTV -- Multivariable control, MIMO; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2019.03.002
• Languages: English
• ISSNs: 0967-0661

Abstract

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%.