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Machine Learning-Based Turbine Vane Position Estimation for Advanced Engine Airpath Control

Journal Article
03-14-06-0050
ISSN: 1946-3936, e-ISSN: 1946-3944
DOI: https://doi.org/10.4271/03-14-06-0050
Published July 29, 2021 by SAE International in United States
Machine Learning-Based Turbine Vane Position Estimation for Advanced Engine Airpath Control
Sector:
Citation: Kamath, R., Venkobarao, V., Kopold, R., and Subramaniam, C., "Machine Learning-Based Turbine Vane Position Estimation for Advanced Engine Airpath Control," SAE Int. J. Engines 14(6):833-851, 2021, https://doi.org/10.4271/03-14-06-0050.
Language: English

References

  1. Dambrosio , L. , Pascazio , G. , and Fortunato , B. Vgt Turbocharger Controlled by an Adaptive Technique IEEE ASME Transactions on Mechatronics 8 4 2007 492 499
  2. Rollinger , J.E. et al. 2012
  3. Tang , H. et al. A Novel Predictive Semi-Physical Feed-Forward Turbocharging System Transient Control Strategy Based on Mean-Value Turbocharger Model Int. J. Eng. Res. 18(8) 8 2017 765 775
  4. Zeng , T. and Zhu , G.G. Control-Oriented Turbine Power Model for a Variable-Geometry Turbocharger Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 232(4) 8 2018 466 481
  5. Kamath , R. , Venkobarao , V. , Kopold , R. , and Subramaniam , C. Neural Network Based Hybridized Dynamic Models for Connected Vehicles - A Case Study on Turbocharger Position Prediction SAE Technical Paper 2019-28-2443 2019 https://doi.org/10.4271/2019-28-2443
  6. Shaaban , S. 2004
  7. Golloch , R. Downsizing in Internal Combustion Engines VDI Book Berlin/Heidelberg Springer-Verlag 2005
  8. Malobabic , M. and Rautenberg , M. Operating and Control Behavior of Car Turbochargers with “Variable Geometry” Rautenberg , M. Charging of Internal Combustion Engines. Advances in vehicle technology 6 Wiesbaden. Vieweg + Teubner Verlag 1990
  9. Moraal , P. and Kolmanovsky , I. Turbocharger Modeling for Automotive Control Applications SAE Technical Paper 1999-01-0908 1999 https://doi.org/10.4271/1999-01-0908
  10. Warner , B. 2017
  11. Kopold , R. 2014
  12. Banks , J. , Nelson , B. , Carson , J. , and Nicol , D. Chapter 9 : Discrete-Event System Simulation Pearson , E. Indian Subcontinent Adaptation 4th ed. India Dorling Kindersley 2007
  13. Kreyszig , E. Advanced Engineering Mathematics New York John Wiley & Sons 2010
  14. Hagan , M.T. and Demuth , H.B. Training Feedforward Networks with the Marquardt Algorithm IEEE Transactions on Neural Networks 5(6) 8 1994 989 993
  15. Hagan , M.T. and Demuth , H.B. Neural Networks for Control Proceedings of the American Control Conference 8 1999 1642 1656
  16. Levenberg , K. A Method for the Solution of Certain Non-Linear Problems in Least Squares The Quarterly of Applied Mathematics 2 8 1944 164 168
  17. Narendra , K.S. and Parthasarathy , K. Identification and Control of Dynamical Systems Using Neural Networks IEEE Trans. Neural Netw. 1(1) 8 1990 4 27
  18. Jafaria , A.H. and Hagan , M.T. Application of New Training Methods for Neural Model Reference Control Engineering Applications of Artificial Intelligence 74 2018 312 321
  19. Kohonen , T. The Self-Organizing Map Knowledge and Data Engineering, IEEE Transactions 78 8 1990 1464 1480
  20. He , H. , Bai , Y. , Garcia , E.A. , and Li , S. Adasyn: Adaptive Synthetic Sampling Approach for Imbalanced Learning International Joint Conference on Neural Networks 2008 1322 1328
  21. He , H. and Garcia , E.A. Learning from Imbalanced Data Knowledge and Data Engineering, IEEE Transactions 21 8 2009 1263 1284
  22. Chawla , N.V. , Bowyer , K.W. , Hall , L.O. , and Kegelmeyer , W.P. Smote: Synthetic Minority Over-Sampling Technique Journal of Artificial Intelligence Research 16 8 2002 321 357
  23. Diaconescu , E. The Use of Narx Neural Networks to Predict Chaotic Time Series WSEAS Transactions on Computer Research 3 2008 182 191

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