Open Access
Issue
MATEC Web of Conferences
Volume 166, 2018
The 2nd International Conference on Mechanical, Aeronautical and Automotive Engineering (ICMAA 2018)
Article Number 02001
Number of page(s) 7
Section Vehicle Design and System Control Engineering
DOI https://doi.org/10.1051/matecconf/201816602001
Published online 23 April 2018
  1. Marchesin, F.P.; Barbosa, R.S.; Alves, M.A.L.; Gadola, M.; Chindamo, D.; Benini, C. (2016). Upright mounted pushrod: the effects on racecar handling dynamics. The Dynamics of Vehicles on Roads and Tracks. Proceedings of the 24th Symposium of the International Association for Vehicle System Dynamics, IAVSD 2015. 543-552. [Google Scholar]
  2. Benini, C; Gadola, M; Chindamo, D; Uberti, S; Marchesin, F.P.; Barbosa, R.S. (2017). The influence of suspension components friction on race car vertical dynamics. Vehicle System Dynamics. 55(3): 338-350. [CrossRef] [Google Scholar]
  3. Gadola, M., Chindamo, D., Romano, M., Padula, F., Development and Validation of a Kalman Filter-Based model for Vehicle Slip Angle Estimation, Vehicle System Dynamics, 2004, 52(1), 68-84. [Google Scholar]
  4. H. Du, J. Lam, K.-C. Cheung W. Li, N. Zhang, Side-slip angle estimation and stability control for a vehicle with a non-linear tyre model and a varying speed, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 229 (2015) 486-505. [CrossRef] [Google Scholar]
  5. D.W. Pi, N. Chen, J.X. Wang, B.J. Zhang, Design and evaluation of sideslip angle observer for vehicle stability control, International Journal of Automotive Technology, 12 (2011) 391-399. [CrossRef] [Google Scholar]
  6. Chindamo, D., Economou, J.T., Gadola, M., Knowles, K., A neurofuzzy-controlled power management strategy for a series hybrid electric vehicle, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2014, 228(9), 1034-1050. [CrossRef] [Google Scholar]
  7. A. Lucas, M. Duran, M. Carmona, M. Lapuerta, Modeling diesel particulate emissions with neural networks, Fuel 4 (2001) 548–593. [Google Scholar]
  8. M. Gwak, K. Jo, M. Sunwoo, Neural-network multiple models filter (NMM)-based position estimation system for autonomous vehicles, International Journal of Automotive Technology, 14 (2013) 265-274. [CrossRef] [Google Scholar]
  9. V. Cirovic, D. Aleksendric, D. Smiljanic, Longitudinal wheel slip control using dynamic neural networks, Mechatronics, 23 (2013) 135-146. [Google Scholar]
  10. Crema, C.; Depari, A.; Flammini, A.; Vezzoli, A.; Benini, C.; Chindamo, D.; Gadola, M.; Romano, M. (2015). Smartphone-based system for vital parameters and stress conditions monitoring for non-professional racecar drivers. Proceedings of the 2015 IEEE SENSORS. 7370521. [Google Scholar]
  11. M. Gadola, D. Vetturi, A. Magalini, Use of an innovative multi-input one-output neural network for experimental data numerical description, 7th International Research/Expert Conference – TMT 2003, Lloret de Mar, Barcelona, Spain, September 2003. [Google Scholar]
  12. H.Sasaki, T.Nishimaki, A side-slip angle estimation using neural network for a wheeled vehicle, SAE Technical Paper 2000-01-0695, 2000. [Google Scholar]
  13. Wang Wei, Bei Shaoyi, Zhang Lanchun, Zhu Kai, Wang Yongzhi, and HangWeixing, Vehicle Sideslip Angle Estimation Based on General Regression Neural Network, Mathematical Problems in Engineering, Volume 2016, Article ID 3107910, 7 pages. [Google Scholar]
  14. Dal Bianco, N.; Lot, R.; Gadola, M., Minimum time optimal control simulation of a GP2 race car. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. Article in press. [Google Scholar]
  15. M. Kato, K. Isoda, H. Yuasa, Estimation of vehicle slip angle with artificial neural network. SAE Technical paper, 15 (1994) 73-86. [Google Scholar]
  16. S. Melzi, E. Sabbioni, On the vehicle sideslip angle estimation through neural networks. Mechanical Systems and Signal Processing, 25 (2011) 2005-2019. [CrossRef] [Google Scholar]
  17. P.D. Wesserman, Neural Computing Theory and Practice, Van Nostrand Reinhold, 1989. [Google Scholar]
  18. W-J. QI, P. Zhang, Z-L. Deng, Robust Sequential Covariance Intersection Fusion Kalman Filtering over Multi-agent Sensor Networks with Measurement Delays and Uncertain Noise Variances, Acta Automatica Sinica, 40 (2014) 2632-2642. [CrossRef] [Google Scholar]
  19. A. Parlak, Y. Islamoglu, H. Yasar, A. Egrisogut, Application of Artificial Neural Network to Predict Specific Fuel Consumption and Exhaust Temperature for a Diesel Engine, Applied Thermal Engineering, 26 (2006) 824–828. [CrossRef] [Google Scholar]
  20. M.T. Hagan, H.B. Demuth, M. Beale, Neural Network Design, PWS Publishing Company, Boston, 1995. [Google Scholar]
  21. Levenberg, K., A Method for the Solution of Certain Problems in Least-Squares, Quarterly Applied Math. 2, pp. 164-168, 1944. [CrossRef] [MathSciNet] [Google Scholar]
  22. Marquardt, D., “An Algorithm for Least-Squares Estimation of Nonlinear Parameters,” SIAM Journal Applied Math., Vol. 11, pp. 431-441, 1963. [CrossRef] [MathSciNet] [Google Scholar]
  23. M. Aydinalp, V.I. Ugursal, A.S. Fung, Predicting residential appliance, lighting, and space cooling energy consumption using neural networks. The Fourth International Thermal Energy Congress, Cesme, Turkey, July. [Google Scholar]
  24. S. Haykin, Neural Networks, A comprehensive foundation, McMillian College Publishing Company, New York, 1994. [Google Scholar]

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