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

R. Hedinger (Corresponding author), N. Zsiga, M. Salazar, C. Onder

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

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%.
Original languageEnglish
Pages (from-to)17-25
Number of pages9
JournalControl Engineering Practice
Volume87
DOIs
Publication statusPublished - 1 Jun 2019
Externally publishedYes

Keywords

  • Catalyst heating mode
  • Iterative learning control, ILC
  • Linear parameter-varying system, LPV
  • Linear time-varying system, LTV
  • Multivariable control, MIMO

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