An engine is a complex system that requires a control strategy to perform optimally and reliably. The majority of the existing system uses a lookup table-based control strategy, also known as feedforward tables, generated offline by performing engine calibration, which is usually an expensive process. The calibration cost can be significantly reduced using a system model, but due to the increased engine complexity, developing a physics-based model that can capture all the system modes becomes challenging. Therefore, the current work uses a data-driven approach to model a diesel engine and designs its control strategy based on it. It uses Gaussian Process Regression (GPR) to model the engine and perform system inversion to achieve the desired optimal feedforward control. Two control inputs, namely, pilot injection timing (PIT) and main injection timing (MIT), are calculated to achieve the desired combustion performance in terms of CA50 from the engine. Model inversion is done using the real-variable genetic algorithm (rGA) to obtain the optimal control strategy corresponding to the desired CA50. The study tries to address two fundamental issues for developing a data-driven control strategy for a practical system: when the model is developed using limited number of data points, and the inversion problem when multiple control settings generate the same optimal output. Finally, the proposed control strategy is validated on an actual experimental test bench, and the results show excellent performance in achieving the desired CA50 value.
|Original language||English (US)|
|Number of pages||6|
|State||Published - 2022|
|Event||2nd Modeling, Estimation and Control Conference, MECC 2022 - Jersey City, United States|
Duration: Oct 2 2022 → Oct 5 2022
Bibliographical noteFunding Information:
The work is funded by the Army Research Laboratory (ARL). Authors would like to thank the ARL for the financial support, and the Engine Research Center team of the University of Wisconsin-Madison for their support in the experimental study.
© 2022 Elsevier B.V.. All rights reserved.
- Feedforward Control
- Gaussian Process Regression
- Genetic Algorithm
- Inverse Mapping