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Mathematical Problems in Engineering
Volume 2014, Article ID 786492, 15 pages
http://dx.doi.org/10.1155/2014/786492
Research Article

Development and Verification of the Tire/Road Friction Estimation Algorithm for Antilock Braking System

1State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
2Key Laboratory of Bionic Engineering of Ministry of Education, Jilin University, Changchun 130022, China

Received 8 June 2014; Revised 6 August 2014; Accepted 6 August 2014; Published 28 September 2014

Academic Editor: Ebrahim Momoniat

Copyright © 2014 Jian Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. Park, D. Hwang, K. Lee et al., “Development of HILS system for ABS ECU of commercial vehicles,” SAE Technical Paper 2001-01-3186, 2001. View at Google Scholar
  2. M. Choi, J. J. Oh, and S. B. Choi, “Linearized recursive least squares methods for real-time identification of tire-road friction coefficient,” IEEE Transactions on Vehicular Technology, vol. 62, no. 7, pp. 2906–2918, 2013. View at Google Scholar
  3. M. Ohori, T. Ishizuka, T. Fujita, N. Masaki, and Y. Suizu, “Fundamental study of smart tire system,” in Proceedings of the IEEE Intelligent Transportation Systems Conference, pp. 1519–1524, Toronto, Canada, September 2006. View at Scopus
  4. A. J. Tuononen, “Optical position detection to measure tyre carcass deflections,” Vehicle System Dynamics, vol. 46, no. 6, pp. 471–481, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Zhuang, K. Guo, and Y. Chen, “Research on the in-tire sensing technology applied in tire-road friction status recognition,” Automobile Technology, no. 6, pp. 11–12, 2010. View at Google Scholar
  6. C. S. Ahn, Robust estimation of road friction coefficient for vehicle active safety systems [Ph.D. thesis], Department of Mechanical Engineering, University of Michigan, Ann Arbor, Mich, USA, 2011.
  7. L. Li, F.-Y. Wang, and Q. Zhou, “Integrated longitudinal and lateral tire/road friction modeling and monitoring for vehicle motion control,” IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 1, pp. 1–19, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. P. P. Lin, M. Ye, and K.-M. Lee, “Intelligent observer-based road surface condition detection and identification,” in Proceeding of the IEEE International Conference on Systems, Man and Cybernetics (SMC ’08), pp. 2465–2470, Singapore, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. O. Nishihara and K. Masahiko, “Estimation of road friction coefficient based on the brush model,” Journal of Dynamic Systems, Measurement and Control, vol. 133, no. 4, Article ID 041006, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Kim and S. Kim, “Estimation of lateral tire force from objective measurement data for handling analysis,” SAE International Journal of Passenger Cars-Mechanical Systems, vol. 6, no. 2, pp. 495–505, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Li, H. Li, J. Song, C. Yang, and H. Wu, “Road friction estimation under complicated maneuver conditions for active yaw control,” Chinese Journal of Mechanical Engineering, vol. 22, no. 4, pp. 514–520, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. L. Imsland, H. Grip, T. Johansen et al., “Nonlinear observer for vehicle velocity with friction and road bank angle adaptation—validation and comparison with an extended Kalman filter,” SAE Technical Paper 2007-01-0808, 2007. View at Google Scholar
  13. M. Wilkin, M. Levesley, and W. Manning, “Design and verification of an extended Kalman filter to estimate vehicle tyre forces,” SAE Technical Paper 2006-01-1285, 2006. View at Google Scholar
  14. M. C. Best, T. J. Gordon, and P. J. Dixon, “Extended adaptive Kalman filter for real-time state estimation of vehicle handling dynamics,” Vehicle System Dynamics, vol. 34, no. 1, pp. 57–75, 2000. View at Publisher · View at Google Scholar · View at Scopus
  15. X. Gao and Z. Yu, “Nonlinear estimation of vehicle sideslip angle based on adaptive extended Kalman filter,” SAE Technical Paper 2010-01-0117, 2010. View at Google Scholar
  16. K. Buckholtz, “Reference input wheel slip tracking using sliding mode control,” SAE Technical Paper 2002-01-0301, 2002. View at Google Scholar
  17. S. Müller, M. Uchanski, and K. Hedrick, “Estimation of the maximum tire-road friction coefficient,” Journal of Dynamic Systems, Measurement and Control, vol. 125, no. 4, pp. 607–617, 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. L. Fan, B. Zhou, and H. Zheng, “A new control strategy for electric power steering on low friction roads,” SAE International Journal of Passenger Cars—Mechanical Systems, vol. 7, no. 3, pp. 972–980, 2014. View at Publisher · View at Google Scholar
  19. G. Heinrich and M. Klüppel, “Rubber friction, tread deformation and tire traction,” Wear, vol. 265, no. 7-8, pp. 1052–1060, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Villagra, B. d'Andréa-Novel, M. Fliess, and H. Mounier, “A diagnosis-based approach for tire-road forces and maximum friction estimation,” Control Engineering Practice, vol. 19, no. 2, pp. 174–184, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. L. Li, H. Li, X. Zhang, L. He, and J. Song, “Real-time tire parameters observer for vehicle dynamics stability control,” Chinese Journal of Mechanical Engineering, vol. 23, no. 5, pp. 620–626, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. M. Geist and O. Pietquin, “Statistically linearized recursive least squares,” in Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP ’10), pp. 272–276, Kittilä, Finland, August-September 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. S. Rhode and F. Gauterin, “Online estimation of vehicle driving resistance parameters with recursive least squares and recursive total least squares,” in Proceedings of the IEEE Intelligent Vehicles Symposium (IV ’13), pp. 269–276, Gold Coast, Australia, June 2013. View at Publisher · View at Google Scholar
  24. M. Bian, L. Chen, Y. Luo, and K. Li, “A dynamic model for tire/road friction estimation under combined longitudinal/lateral slip situation,” SAE Technical Paper 2014-01-0123, 2014. View at Google Scholar
  25. H. B. Pacejka, Tyre and Vehicle Dynamic, Butterworth-Heinemann, 2nd edition, 2005.
  26. Z. Lin, W. Xiaoxu, L. Liang et al., Nonlinear System Filtering Theory, National Defense Industry Press, Beijing, China, 2012.
  27. K. Singh, M. Arat, and S. Taheri, “Enhancement of collision mitigation braking system performance through real-time estimation of tire-road friction coefficient by means of smart tires,” SAE International Journal of Passenger Cars—Electronic and Electrical Systems, vol. 5, no. 2, pp. 607–624, 2012. View at Publisher · View at Google Scholar
  28. L. Jinkun, Sliding Mode Control Design and Matlab Simulation, Tsinghua University Press, Beijing, China, 2nd edition, 2012.