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Nonlinear Identification of Electronic Brake Pedal Behavior Using Hybrid GMDH and Genetic Algorithm in Brake-By-Wire System

  • Bae, Junhyung (Dept. of Information and Communication Engineering, DGIST) ;
  • Lee, Seonghun (Convergence Research Center for Future Automotive Technology, DGIST) ;
  • Shin, Dong-Hwan (Convergence Research Center for Future Automotive Technology, DGIST) ;
  • Hong, Jaeseung (Convergence Research Center for Future Automotive Technology, DGIST) ;
  • Lee, Jaeseong (Convergence Research Center for Future Automotive Technology, DGIST) ;
  • Kim, Jong-Hae (Dept. of Electronic and Electrical Engineering, Catholic University of Daegu)
  • Received : 2016.05.26
  • Accepted : 2017.02.16
  • Published : 2017.05.01

Abstract

In this paper, we represent a nonlinear identification of electronic brake pedal behavior in the brake-by-wire (BBW) system based on hybrid group method of data handling (GMDH) and genetic algorithm (GA). A GMDH is a kind of multi-layer network with a structure that is determined through training and which can express nonlinear dynamics as a mathematical model. The GA is used in the GMDH, enabling each neuron to search for its optimal set of connections with the preceding layer. The results obtained with this hybrid approach were compared with different nonlinear system identification methods. The experimental results showed that the hybrid approach performs better than the other methods in terms of root mean square error (RMSE) and correlation coefficients. The hybrid GMDH/GA approach was effective for modeling and predicting the brake pedal system under random braking conditions.

Keywords

References

  1. S. Anwar, Fault tolerant drive by wire systems: Impact on vehicle safety and reliability, Bentham Science Publishers, 2012.
  2. S. Anwar and L. Chen, "An analytical redundancy based fault detection and isolation algorithm for a road-wheel control subsystem in a steer-by-wire system," IEEE Trans. on Vehicular Technology, vol. 56, no. 5, pp. 2859-2869, Sept. 2007. https://doi.org/10.1109/TVT.2007.900515
  3. A. Higgins and S. Koucky, "Mercedes pumps fly-bywire brakes into new SL roadster," Machine Design, vol. 74, no. 9, p. 26, May 2002.
  4. R. Isermann, R. Schwarz and S. Stolzl, "Fault tolerant drive-by-wire systems," IEEE Control Syst. Magazine, pp. 64-81, Oct. 2002.
  5. Y. Ki, H. Ahn and J. Cheon, "Fault-tolerant control of EMB systems," SAE Int. Journal of Passenger Cars - Electronic and Electrical Systems, vol. 5, no. 2, pp. 579-589, Sept. 2012. https://doi.org/10.4271/2012-01-1795
  6. W. Hwang and K. Huh, "Fault detection and estimation for electromechanical brake systems using parity space approach," Journal of Dynamic Systems, Measurement and Control, vol. 137, no. 1, Jan. 2015.
  7. W. Hwang, K. Han, K. Huh, J. Jung and M. Kim, "Model-based sensor fault detection algorithm design for electro-mechanical brake," 4th International IEEE Conf. on Intelligent Transportation Systems, Washington, DC, USA, pp. 962-967, Oct. 2011.
  8. K. Assaleh, T. Shanableh and Y. A. Kheil, "Group method of data handling for modeling magnetorheological dampers," Intelligent Control and Automation, vol. 4, no. 1, pp. 70-79, 2013. https://doi.org/10.4236/ica.2013.41010
  9. T. Lopez-Molina, A. Perez-Mendez and F. Rivas-Echeveria, "Data analysis techniques for neural networks-based virtual sensors," in Proc. of the 8th WSEAS Int. Conf. on Neural Networks, Vancouver, British Columbia, Canada, pp. 76-83, Jun. 2007.
  10. H. Baha and Z. Dibi, "A novel neural network-based technique for smart gas sensors operating in a dynamic environment," Sensors, vol. 9, no. 11, pp. 8944-8960, Nov. 2009. https://doi.org/10.3390/s91108944
  11. A. G. Ivakhnenko, "Polynomial theory of complex systems," IEEE Trans. on Systems, Man, and Cybernetics, vol. SMC-1, no. 4, pp. 364-378, Oct. 1971. https://doi.org/10.1109/TSMC.1971.4308320
  12. D. H. Lim, S. H. Lee and M. G. Na, "Smart softsensing for the feedwater flowrate at PWRs using a GMDH algorithm," IEEE Trans. on Nuclear Science, vol. 57, no. 1, pp.340-347, Feb. 2010. https://doi.org/10.1109/TNS.2009.2035121
  13. S. J. Farlow, "The GMDH algorithm of Ivakhnenko," The American Statistician, vol. 35, no. 4, pp. 210-215, Nov. 1981.
  14. M. Shaverdi, S. Fallahi and V. Bashiri, "Prediction of stock price of Iranian petrochemical industry using GMDH-type neural network and genetic algorithm," Applied Mathematical Sciences, vol. 6, no. 7, pp. 319-332, 2012.
  15. J. H. Kim, Y. Matsui, S. Hayakawa, T. Suzuki, S. Okuma and N. Tsuchida, "Acquisition and modeling of driving skills by using three dimensional driving simulator," IEICE Trans. on Fundamentals of Elec., Comm. and Comp., vol. E88-A, no. 3, pp. 770-778, Mar. 2005. https://doi.org/10.1093/ietfec/e88-a.3.770
  16. Y. W. Kim, R. Matsuda, T. Narikiyo and J. H. Kim, "Attitude control of planar space robot based on selforganizing data mining algorithm," in Proc. of Int. Conf. on Control, Automation and Syst., Gyeonggi, Korea, pp. 377-382, Jun. 2005.
  17. M. Iwasaki, H. Takei and N. Matsui, "GMDH-based modeling and feedforward compensation for nonlinear friction in table drive systems," IEEE Trans. on Industrial Electronics, vol. 50, no. 6, pp. 1172-1178, Dec. 2003. https://doi.org/10.1109/TIE.2003.819698
  18. N. Nariman-Zadeh, A. Darvizeh, A. Jamali and A. Moeini, "Evolutionary design of generalized polynomial neural networks for modeling and prediction of explosive forming process," Journal of Material Processing and Technology, vol. 164-165, pp. 1516-1571, May 2005.
  19. N. Nariman-Zadeh and A. Jamali, "Pareto genetic design of GMDH-type neural networks for nonlinear systems," in Proc. of the Int. Workshop on Inductive Modeling, Prague, Czech Republic, pp. 96-103, 2006.
  20. G. C. Onwubolu, Hybrid self-organizing modeling systems, Springer-Verlag Berlin Heidelberg, 2009.
  21. R. Hoseinnezhad and A. Bab-Hadiashar, "Missing data compensation for safety-critical components in a drive-by-wire system," IEEE Trans. on Vehicular Technology, vol. 54, no. 4, pp. 1304-1311, Jul. 2005. https://doi.org/10.1109/TVT.2005.851331