Dynamic engine modeling through linear programming support vector regression

Authors:
Zhao Lu;Jing Sun;Dongkyoung Lee;Ken Butts
Affiliations:
Electrical Engineering Department, Tuskegee University, Tuskegee, Alabama;Department of Naval Architecture and Marine Engineering and the Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI;Department of Naval Architecture and Marine Engineering and the Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI;North America Technical Center, Toyota Motor Corporation, Ann Arbor, Michigan
Venue:
ACC'09 Proceedings of the 2009 conference on American Control Conference
Year:
2009

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Abstract

In this paper, we develop a dynamic model for an internal combustion engine using Support Vector Regression (SVR). In particular, a linear programming SVR (LP-SVR) approach is investigated. The computational advantages and generalization capability of the LP-SVR dynamic engine model are illustrated through a case study, where a model is developed for an L4 gasoline engine. Simulation results are reported to demonstrate the effectiveness of proposed approach and to illustrate the trade-offs among different modeling attributes.