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A System Identification Framework for Modeling Complex Combustion Dynamics Using Support Vector Machines

  • Vijay Manikandan Janakiraman
  • XuanLong Nguyen
  • Jeff Sterniak
  • Dennis Assanis
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 283)

Abstract

Machine Learning is being widely applied to problems that are difficult to model using fundamental building blocks. However, the application of machine learning in powertrain modeling is not common because existing powertrain systems have been simple enough to model using simple physics. Also, black box models are yet to demonstrate sufficient robustness and stability features for widespread powertrain applications. However, with emergence of advanced technologies and complex systems in the automotive industry, obtaining a good physical model in a short time becomes a challenge and it becomes important to study alternatives. In this chapter, support vector machines (SVM) are used to obtain identification models for a gasoline homogeneous charge compression ignition (HCCI) engine. A machine learning framework is discussed that addresses several challenges for identification of the considered system that is nonlinear and whose region of stable operation is very narrow.

Keywords

Support vector machine Identification Combustion Homogeneous charge compression ignition HCCI Neural networks Nonlinear regression Engine model Control model 

References

  1. 1.
    Aoyama, T., Hattori, Y., Mizuta, J., Sato, Y.: An experimental study on premixed-charge compression ignition gasoline engine. In: SAE Technical Paper 960081 (1996).Google Scholar
  2. 2.
    Ravi, N., Roelle, M.J., Liao, H.H., Jungkunz, A.F., Chang, C.F., Park, S., Gerdes, J.C.: Model-based control of hcci engines using exhaust recompression. IEEE Trans. Control Syst. Technol. 131, 5 (2009)Google Scholar
  3. 3.
    Bengtsson, J., Strandh P., Johansson R., Tunestal P., Johansson, B.: Model predictive control of homogeneous charge compression ignition (HCCI) engine dynamics. In: 2006 IEEE International Conference on Control Applications (2006)Google Scholar
  4. 4.
    Bloch, G., Lauer, F., Colin, G.: On learning machines for engine control. Comput. Int. Automo. Appl. 132, 125–144 (2008)Google Scholar
  5. 5.
    Janakiraman, V.M., Sterniak, J., Assanis, D.: Support vector machines for identification of HCCI combustion dynamics. International Conference on Informatics in Control, Automation and Robotics (ICINCO), Rome, In (2012)Google Scholar
  6. 6.
    Hammer, B., Gersmann, K.: A note on the universal approximation capability of support vector machines. In: Neural Processing Letters. Kluwer Academic Publishers, Boston (2003)Google Scholar
  7. 7.
    Clarke, S.M., Griebsch, J.H., Simpson, T.W.: Analysis of support vector regression for approximation of complex engineering analyses. J. Mech. Des. 127, 1077–1087 (2005)CrossRefGoogle Scholar
  8. 8.
    Wang, X., Chen, DuZ, J., Pan, F.: Dynamic modeling of biotechnical process based on online support vector machine. J. Comput. 4(3), 251–258 (2009). http://ojs.academypublisher.com/index.php/jcp/article/view/0403251258
  9. 9.
    Chitralekha, S.B., Shah, S.L.: Application of support vector regression for developing soft sensors for nonlinear processes. The Can. J. Chem. Eng. 88(6), 899–911 (2010)CrossRefGoogle Scholar
  10. 10.
    Chitralekha, S.B., Shah, S.L.: Application of Support Vector Regression for Developing Soft Sensors for Nonlinear Processes. Wiley, New York (2010)Google Scholar
  11. 11.
    Müller, K.R., Smola, A.J., Ratsch, G., Scholkopf, B., Kohlmorgen, J., Vapnik, V.: Predicting time series with support vector machines. In: Artificial Neural Networks—ICANN’97. Springer, Berlin (1997)Google Scholar
  12. 12.
    Sun, J., Zhou, Y., Bai, Y., Luo, J.: Nonlinear noise reduction of chaotic time series based on multi-dimensional recurrent least squares support vector machines. In: Neural Information Processing, LNCS. Springer, Heidelberg (2006)Google Scholar
  13. 13.
    Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., Vapnik, V.: Support Vector Regression Machines (1996)Google Scholar
  14. 14.
    Schölkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. In: Neural Computation. MIT Press, Cambridge (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Vijay Manikandan Janakiraman
    • 1
  • XuanLong Nguyen
    • 1
  • Jeff Sterniak
    • 2
  • Dennis Assanis
    • 3
  1. 1.University of MichiganAnn ArborUSA
  2. 2.Robert Bosch LLCFarmingtonUSA
  3. 3.Stony Brook UniversityNYUSA

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