Identification of Tire Force Model Using Experimental Data of a New Scaled Test Rig for Design of Nonlinear Slip Controller

Document Type : Research Article

Authors

Department of Mechanical Engineering, Sahand University of Technology, East Azerbaijan, Iran

Abstract

In this paper, three models for tire friction force are identified using experimental data of scaled test rig. In this setup, a scaled tire is forced to be in contact with a high inertia disk and the friction force between the tire and disk is measured in terms of the slip during braking. For identification of the models, tire’s rotational speed, tire’s slip and tire’s normal force are used as the inputs and the tire’s longitudinal force is considered as the output. The experimental data required for identification are collected by force and rotational displacement sensors. By using the measured data, the parameters of tire friction force models are calculated using nonlinear least square method. The identified models are evaluated by data not used in the identification process. The results show that the identified models follow the system outputs with acceptable errors. Among the identified models, the Dugoff model has the better accuracy compared with the Fiala and semi-linear models. As an application, a nonlinear dynamic model of the setup including the identified friction force model is employed to design a nonlinear controller for anti-lock braking system.

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References

[1] K. Guo, D. Lu, S. Chen, W. Lin, X. Lu, The UniTire model: a nonlinear and non-steady-state tyre model for vehicle dynamics simulation, Vehicle system dynamics, 43(1) (2005) 341-358.
[2] W. Kong, Design and Fabrication of A Reduced Scale Tire Test Machine, University Tecnical Malaysia Melaka, 2013.
[3] R. Chaichaowarata, W. Wannasuphoprasit, Tire test for drifting dynamics of a scaled vehicle, Journal of Research and Applications in Mechanical Engineering, 1(3) (2013) 33-39.
[4] K. Guo, D. Lu, UniTire: unified tire model for vehicle dynamic simulation, Vehicle System Dynamics, 45(S1) (2007) 79-99.
[5] S. Garatti, S. Bittanti, Parameter estimation in the Pacejka's tyre model through the TS method, IFAC Proceedings Volumes, 42(10) (2009) 1304-1309.
[6] F. Gustafsson, Slip-based tire-road friction estimation, Automatica, 33(6) (1997) 1087-1099.
[7] W. Grimes, J. Balasa, E. Hunter, T. Vadnais, Extracting Tire Modal Parameters from Test Data SAE Technical Paper, 2006-01-1399 (2006).
[8] V. Cossalter, A. Doria, E. Giolo, L. Taraborrelli, M. Massaro, Identification of the characteristics of motorcycle and scooter tyres in the presence of large variations in inflation pressure, Vehicle System Dynamics, 52(10) (2014) 1333-1354.
[9] P. Lugner, H. Pacejka, M. Plöchl, Recent advances in tyre models and testing procedures, Vehicle System Dynamics, 43(6-7) (2005) 413-426.
[10] A. Alagappan, K. Rao, R. Kumar, A comparison of various algorithms to extract Magic Formula tyre model coefficients for vehicle dynamics simulations, Vehicle System Dynamics, 53(2) (2015) 154-178.
[11] D. Tan, Y. Wang, L. Zhang, Research on the parameter identification of LuGre tire model based on genetic algorithms, in: International Conference on Intelligent Systems and Knowledge Engineering, Atlantis Press, 2007.
[12] C. Canudas-de-Wit, P. Tsiotras, E. Velenis, M. Basset, G. Gissinger, Dynamic friction models for road/tire longitudinal interaction, Vehicle System Dynamics, 39(3) (2003) 189-226.
[13] K. Guo, L. Ren, A unified semi-empirical tire model with higher accuracy and less parameters, SAE transactions, (1999) 1513-1520.
[14] A. Farhat, D. Koenig, D. Hernandez-Alcantara, R. Morales-Menendez, Tire force estimation using a proportional integral observer, Journal of Physics: Conference Series 783(1) (2017) 1-11.
[15] E. Sabbioni, R. Bao, F. Cheli, D. Tarsitano, A particle filter approach for identifying tire model parameters from full-scale experimental tests, Journal of Mechanical Design, 139(2) (2017) 1-7.
[16] Y.H. Liu, T. Li, Y.Y. Yang, X. Ji, J. Wu, Estimation of tire-road friction coefficient based on combined APF-IEKF and iteration algorithm, Mechanical Systems and Signal Processing, 88 (2017) 25-35.
[17] J.Y. Wong, Theory of ground vehicles, John Wiley & Sons, New York, 2008.
[18] E. Fiala, Lateral forces on rolling pneumatic tires, Zeitschrift VDI 96(29) (1954) 973-979.
[19] S. Kuntanapreeda, Estimation of Longitudinal Tire Force Using Nonlinearity Observer, Open Journal of Applied Sciences, 3(2) (2013) 41-46.
[20] J. Svendenius, Tire modeling and friction estimation, Lund University, Sweden, 2007.
[21] M. Karari, System Identification, Amirkabir University of Technology Publication, Tehran, 2014.
[22] M. Radac, R. Precup, S. Preitl, J. Tar, E. Petriu, Linear and fuzzy control solutions for a laboratory anti-lock braking system, in: 6th International Symposium on Intelligent Systems and Informatics, IEEE, Subotica, Serbia, 2008, pp. 1-6.
[23] H. Mirzaeinejad, M. Mirzaei, A novel method for non-linear control of wheel slip in anti-lock braking systems, Control Engineering Practice, 18(8) (2010) 918-926.
Amirkabir Journal of Mechanical Engineering
Volume 51, Issue 1
March and April 2019
Pages 187-200

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