Name
Title | ||
|---|---|---|
Data-driven Modeling and Predictive Control of Maximum Pressure Rise Rate in RCCI Engines |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CCTA41146.2020.9206358 | 2020 IEEE Conference on Control Technology and Applications (CCTA) |
Keywords | DocType | ISBN |
Engines,Combustion,Solid modeling,Support vector machines,Fuels,Data models,Predictive models | Conference | 978-1-7281-7140-1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| L. N. Aditya Basina | 1 | 0 | 0.34 |
| Behrouz K. Irdmousa | 2 | 0 | 0.34 |
| Javad Mohammadpour Velni | 3 | 0 | 0.34 |
| Hoseinali Borhan | 4 | 0 | 0.34 |
| Jeffrey D. Naber | 5 | 0 | 0.34 |
| Mahdi Shahbakhti | 6 | 0 | 0.34 |