Model-based iterative learning control strategies for precise trajectory tracking in gasoline engines
| Autor: | Raffael Hedinger, Norbert Zsiga, Mauro Salazar, Christopher H. Onder |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Computer science Applied Mathematics 020208 electrical & electronic engineering Iterative learning control Feed forward 02 engineering and technology Tracking (particle physics) Computer Science Applications Reduction (complexity) 020901 industrial engineering & automation Control and Systems Engineering Control theory Path (graph theory) 0202 electrical engineering electronic engineering information engineering Trajectory Ignition timing Physics::Chemical Physics Electrical and Electronic Engineering Actuator |
| Zdroj: | Control Engineering Practice. 87:17-25 |
| ISSN: | 0967-0661 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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