Logical control scheme with real-time statistical learning for residual gas fraction in IC engines

Research Paper Special Focus on Analysis and Control of Finite-Valued Network Systems
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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.

Keywords

combustion engine statistical learning residual gas fraction variable valve timing logical control 

Notes

Acknowledgements

The authors gratefully acknowledge the support and generosity of Toyota Motor Corporation, without which the present study could not have been completed.

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Engineering and Applied SciencesSophia UniversityTokyoJapan
  2. 2.School of Control Science and EngineeringDalian University of TechnologyDalianChina

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