Release Time:2019-03-11  Hits:

Indexed by: Journal Article

Date of Publication: 2018-01-01

Journal: SCIENCE CHINA-INFORMATION SCIENCES

Included Journals: Scopus、EI、SCIE

Volume: 61

Issue: 1

ISSN: 1674-733X

Key Words: combustion engine; statistical learning; residual gas fraction; variable valve timing; logical control

Abstract: In this paper, an optimal control scheme for reducing the fluctuation of residual gas fraction (RGF) under variational operating condition is developed by combining stochastic logical system approach with statistical learning method. The method estimating RGF from measured in-cylinder pressure is introduced firstly. Then, the stochastic properties of the RGF are analyzed according to statistical data captured by conducting experiments on a test bench equipped with a L4 internal combustion engine. The influences to the probability distribution of the RGF from both control input and environment parameters are also analyzed. Based on the statistical analysis, a stochastic logical transient model is adopted for describing cyclic behavior of the RGF. Optimal control policy maps for different fixed operating conditions are calculated then. Besides, a statistical learning-based method is applied to learn the probability density function (PDF) of RGF in the real-time which is used to adjust the control MAP based on logical optimization. The whole optimal control policy map is obtained based on Gaussian process regression with consideration of statistical information of RGF. Finally, the performance of the proposed method is experimentally validated.