Data-driven modeling and predictive control of maximum pressure rise rate in RCCI engines
Document Type
Conference Proceeding
Publication Date
9-28-2020
Department
Department of Mechanical Engineering-Engineering Mechanics
Abstract
Reactivity controlled compression ignition (RCCI) is a promising low temperature combustion (LTC) regime that offers lower nitrogen oxides (NOx), soot and particulate matter (PM) emissions along with higher combustion efficiency compared to conventional diesel engines. It is critical to control maximum pressure rise rate (MPRR) in RCCI engines in order to safely and efficiently operate at varying engine loads. In this paper, a data-driven modeling (DDM) approach using support vector machines (SVM) is adapted to develop a linear parameter-varying (LPV) representation of MPRR for RCCI combustion. This LPV representation is then used in the design of a model predictive controller (MPC) to control crank angle of 50% of fuel mass fraction burn (CA50) and indicated mean effective pressure (IMEP) while limiting the MPRR. The results show that the LPV-MPC control strategy can track CA50 and IMEP with mean tracking errors of 0.9 CAD and 4.7 kPa, respectively, while limiting the MPRR to the maximum allowable value of 5.8 bar/CAD.
Publication Title
CCTA 2020 - 4th IEEE Conference on Control Technology and Applications
ISBN
9781728171401
Recommended Citation
Basina, L.,
Irdmousa, B. K.,
Velni, J.,
Borhan, H.,
Naber, J.,
&
Shahbakhti, M.
(2020).
Data-driven modeling and predictive control of maximum pressure rise rate in RCCI engines.
CCTA 2020 - 4th IEEE Conference on Control Technology and Applications, 94-99.
http://doi.org/10.1109/CCTA41146.2020.9206358
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/14427
Publisher's Statement
© 2020 IEEE. Publisher’s version of record: https://doi.org/10.1109/CCTA41146.2020.9206358