Improving Genetic Algorithm Based Parameter Optimizing LSSVM Model for Tyre Regression

Abstract:

According to the requirement of vehicle dynamics' accurate simulation and control, the paper studies the tyre regression algorithm based on the tyre bench test data. Due to the tyre test's characters of few data and big discreteness, the method of least squares support vector regression (LSSVM), which has the superiority of structural risk minimization, was selected to find the nonlinear mapping between input and output variables of tyre test data. Removing data gross error and improving the sparsification measures were taken to increase the calculation real time of standard LSSVM algorithm. An adaptive genetic algorithm (AGA) with global searching ability was chosen to determine the kernel function and regularization parameters of LSSVM. Test data set’s regression root mean square error (RMSE) was taken as the fitness function of AGA. Finally, the tyre test data under steady state sideslip condition was provided to simulate and verify the effectiveness of tyre regression algorithm, according to the precision and real time requirements of vehicle dynamic simulation and control.

• Periodical: Applied Mechanics and Materials (Volumes 55-57)
• Pages: 1839-1844
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.55-57.1839
• Online since: May 2011
• Authors: Xiao Long Zhang, Liang Li, Jian Song, Dan Dan Sheng
• Keywords: Adaptive Genetic Algorithm (AGA), Automotive Engineering, Least Squares Support Vector Regression (LSSVM), Tyre Test

References

[1] E. Bakker, L. Nyborg, H. B. Pacejka. Tyre Modelling for Use in Vehicle Dynamics Studies. SAE Paper 870421. (1987).

DOI: 10.4271/870421

[2] H. B. Pacejka. Tyre and Vehicle Dynamics. Second Edition. Butterworth-Heinemann of Elsevier, UK. (2006).

[3] J. J. M. Van Oosten, E. Baker. Determination of Magic Tyre model parameters. Vehicle System Dynamics. Vol 21 supplement (1992), pp.19-29.

DOI: 10.1080/00423119208969995

[4] A. Ortiz, J. A. Cabrera, A. J. Guerra and A. Simon. An Alternative Method to Determine the Magic Tyre Model Parameters Using Genetic Algorithms. Vehicle System Dynamics. Vol 41(2004), pp.109-127.

DOI: 10.1076/vesd.41.2.109.26496

[5] J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle. Least Squares Support Vector Machines. World Scientific, Singapore. (2002).

DOI: 10.1142/5089

[6] Hsuan-Tien Lin, Chih-Jen Lin. A Study on Sigmoid Kernels for SVM and Training of non-P SD kernels by SMO-type Methods. Technical report (2003).

[7] V. Vapnik. The nature of statistical learning theory. Springer-Verlag, New York. (1995).

[8] M. Srinivas, L. M. Patnaik. Adaptive probabilities fo crossover and mutation in genetic algorithm. IEEE Trans on System, Man and Cybernetics. Vol. 24(1994), pp.656-667.

DOI: 10.1109/21.286385