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Learning Based Model Predictive Control of Combustion Timing in Multi-Cylinder Partially Premixed Combustion Engine
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 9, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Partially Premixed Combustion (PPC) has shown to be a promising advanced combustion mode for future engines in terms of efficiency and emission levels. The combustion timing should be suitably phased to realize high efficiency. However, a simple constant model based predictive controller is not sufficient for controlling the combustion during transient operation. This article proposed one learning based model predictive control (LBMPC) approach to achieve controllability and feasibility. A learning model was developed to capture combustion variation. Since PPC engines could have unacceptably high pressure-rise rates at different operation points, triple injection is applied as a solvent, with the use of two pilot fuel injections. The LBMPC controller utilizes the main injection timing to manage the combustion timing. The cylinder pressure is used as the combustion feedback. The method is validated in a multi-cylinder heavy-duty PPC engine for transient control.
CitationLi, X., Yin, L., Tunestal, P., and Johansson, R., "Learning Based Model Predictive Control of Combustion Timing in Multi-Cylinder Partially Premixed Combustion Engine," SAE Technical Paper 2019-24-0016, 2019.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
- Okude, K., Mori, K., Shiino, S., and Moriya, T. , “Premixed Compression Ignition (PCI) Combustion for Simultaneous Reduction of NOx and Soot in Diesel Engine,” SAE Technical Paper 2004-01-1907, 2004, doi:10.4271/2004-01-1907.
- Kimura, S., Aoki, O., Ogawa, H., Muranaka, S. et al. , “New Combustion Concept for Ultra-Clean and High-Efficiency Small DI Diesel Engines,” SAE Technical Paper 1999-01-3681, 1999, doi:10.4271/1999-01-3681.
- Noehre, C., Andersson, M., Johansson, B., and Hultqvist, A. , “Characterization of Partially Premixed Combustion,” SAE Technical Paper 2006-01-3412, 2006, doi:10.4271/2006-01-3412.
- Yin, L., Ingesson, G., Shamun, S., Tunestal, P. et al. , “Sensitivity Analysis of Partially Premixed Combustion (PPC) for Control Purposes,” SAE Technical Paper 2015-01-0884, 2015, doi:10.4271/2015-01-0884.
- Maciejowski, J. M. , “Predictive Control: With Constraints,” Pearson education, 2002.
- Bengtssonv, J., Strandhv P., Johansson R., Tunestal P., and Johansson B. , “Model Predictive Control of Homogeneous Charge Compression Ignition (HCCI) Engine Dynamics.” in 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, 1675-80, 2006. doi:10.1109/CACSD-CCA-ISIC.2006.4776893.
- Ingesson, G., Yin, L., Johansson, R., and Tunestal, P. , “A Model-Based Injection-Timing Strategy for Combustion-Timing Control,” SAE Int. J. Engines 8(3):1012-1020, 2015, doi:10.4271/2015-01-0870.
- Rausen, D. J., Stefanopoulou, A. G., Kang, J.-M., Eng, J. A., and Kuo, T.-W. , “A Mean-Value Model for Control of Homogeneous Charge Compression Ignition (HCCI) Engines,” Journal of Dynamic Systems, Measurement, and Control 127(3):355-362, 2005.
- Ghojel, J. I. , “Review of the Development and Applications of the Wiebe Function: A Tribute to the Contribution of Ivan Wiebe to Engine Research,” International Journal of Engine Research 11(4):297-312, 2010.
- Aswani, A., Gonzalez, H., Sastry, S. S., and Tomlin, C. , “Provably Safe and Robust Learning-Based Model Predictive Control,” Automatica 49(5):1216-1226, May 2013, doi:10.1016/j.automatica.2013.02.003.
- Bouffard, P., Aswani A., and Tomlin C. , “Learning-Based Model Predictive Control on a Quadrotor: Onboard Implementation and Experimental Results,” in 2012 IEEE International Conference on Robotics and Automation, 279-84, 2012. doi:10.1109/ICRA.2012.6225035.
- Ostafew, C. J., Schoellig A. P., and Barfoot T. D. . “Learning-Based Nonlinear Model Predictive Control to Improve Vision-Based Mobile Robot Path-Tracking in Challenging Outdoor Environments,” in 2014 IEEE International Conference on Robotics and Automation (ICRA), 4029-36, 2014. doi:10.1109/ICRA.2014.6907444.
- Rosolia, U., Carvalho A., and Borrelli F. . “Autonomous Racing Using Learning Model Predictive Control,” in 2017 American Control Conference (ACC), 5115-20, 2017. https://doi.org/10.23919/ACC.2017.7963748.
- Heywood, John B. , “Internal Combustion Engine Fundamentals,” (1988).
- Yin, Lianhao , “Model Predictive Control (MPC) of an Advanced Multi-Cylinder Engine for Transient Operations,” Department of Energy Sciences, Lund University, 2018.
- Bezanson, Jeff, Stefan Karpinski, Viral B., Shah, and Edelman Alan , “Julia: A Fast Dynamic Language for Technical Computing,” ArXiv:1209.5145 [Cs], September 23, 2012. http://arxiv.org/abs/1209.5145.
- Wächter, A. and Biegler, L. T. , “On the Implementation of an Interior-Point Filter Line-Search Algorithm for Large-Scale Nonlinear Programming,” Mathematical Programming 106(1):25-57, March 1, 2006, doi:10.1007/s10107-004-0559-y.