Nonlinear optimization of a new polynomial tyre model

Tyre behaviour is strongly nonlinear. This article presents the validation of a new polynomial tyre model with real test data, analysing the convergence properties during the optimization process to calculate the values of the parameters. A multivariate model with 13 parameters is shown, including normal load and camber angle. The article reviews the methods of getting polynomial approximations of the magic formula tyre model used to develop the new polynomial model, the numerical optimization methods which calculate the parameters of the model from real test data, and it explains how the terms of the Jacobian matrix are modified when we impose constraints to the curve; this can be useful to improve the adjustment in some areas of the curve. The convergence properties are shown both for the magic formula tyre model and for this polynomial tyre model. The proposed model presents a fast convergence both in one and in three variables. This is an additional advantage to its excellent analytical properties, and the model is very easy to compute and can be easily derived and integrated. It is very well adapted for real-time computing.

[1]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[2]  Sergio Bittanti,et al.  Estimation of white-box model parameters via artificial data generation: a two stage approach , 2008 .

[3]  Rene F. Swarttouw,et al.  Orthogonal polynomials , 2020, NIST Handbook of Mathematical Functions.

[4]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[5]  M. J. D. Powell,et al.  On search directions for minimization algorithms , 1973, Math. Program..

[6]  K. I. M. McKinnon,et al.  Convergence of the Nelder-Mead Simplex Method to a Nonstationary Point , 1998, SIAM J. Optim..

[7]  William C. Davidon,et al.  Variable Metric Method for Minimization , 1959, SIAM J. Optim..

[8]  Antonella Ferrara,et al.  Collision avoidance strategies and coordinated control of passenger vehicles , 2007 .

[9]  R. Fletcher Practical Methods of Optimization , 1988 .

[10]  Alberto López Rosado Predicción de situaciones inseguras en vehículos automóviles , 1994 .

[11]  L. Fox,et al.  Chebyshev polynomials in numerical analysis , 1970 .

[12]  J. Waldvogel Fast Construction of the Fejér and Clenshaw–Curtis Quadrature Rules , 2006 .

[13]  A. López,et al.  Approximations to the magic formula , 2010 .

[14]  C. G. Broyden Quasi-Newton methods and their application to function minimisation , 1967 .

[15]  F. Amirouche,et al.  Dynamic collision avoidance and constraint violation compensation , 1995 .

[16]  C. G. Broyden A Class of Methods for Solving Nonlinear Simultaneous Equations , 1965 .

[17]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[18]  Eric Kvaalen A faster Broyden method , 1991 .

[19]  Richard H. Byrd,et al.  Analysis of a Symmetric Rank-One Trust Region Method , 1996, SIAM J. Optim..

[20]  Hans B. Pacejka,et al.  A New Tire Model with an Application in Vehicle Dynamics Studies , 1989 .

[21]  Irene A. Stegun,et al.  Handbook of Mathematical Functions. , 1966 .

[22]  Antonella Ferrara,et al.  Application of Switching Control for Automatic Pre-Crash Collision Avoidance in Cars , 2006 .

[23]  Hans B. Pacejka,et al.  Tyre Modelling for Use in Vehicle Dynamics Studies , 1987 .

[24]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[25]  M. Powell,et al.  Approximation theory and methods , 1984 .

[26]  J. A. Cabrera,et al.  An Alternative Method to Determine the Magic Tyre Model Parameters Using Genetic Algorithms , 2004 .

[27]  C. W. Clenshaw,et al.  A method for numerical integration on an automatic computer , 1960 .

[28]  Cristina Moriano,et al.  Bivariate Chebyshev Expansion of the Pacejka's Tyre Model , 2007 .

[29]  Cristina Moriano Sánchez Método de procesamiento rápido de las ecuaciones de la dinámica vehicular , 2013 .

[30]  Antonio Moreno Ortiz,et al.  An easy procedure to determine Magic Formula parameters: a comparative study between the starting value optimization technique and the IMMa optimization algorithm , 2006 .

[31]  María Pilar Vélez Melón,et al.  Aplicación de los polinomios de Chebyshev al procesamiento rápido de las ecuaciones de la dinámica vehicular , 2006 .

[32]  Hans B. Pacejka,et al.  Tire and Vehicle Dynamics , 1982 .

[33]  P. K. Rajendrakumar,et al.  Optimal design of multi-parametric nonlinear systems using a parametric continuation based Genetic Algorithm approach , 2012 .

[34]  Ya-Xiang Yuan,et al.  Optimization Theory and Methods: Nonlinear Programming , 2010 .

[35]  J. J. M. Van Oosten,et al.  DETERMINATION OF MAGIC TYRE MODEL PARAMETERS , 1992 .

[36]  Sergio Bittanti,et al.  Parameter estimation in the Pacejka's tyre model through the TS method , 2009 .

[37]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[38]  Alberto López,et al.  Simulation of the suspension of a vehicle using Chebyshev series , 2014 .

[39]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .