Method in identifying the parameters of magic formula tire model based on new self-adaptive differential evolution

被引:0
|
作者
机构
[1] School of Mechanical and Automotive Engineering, South China University of Technology
来源
Wang, Q. (wangqian924@gmail.com) | 1600年 / Chinese Mechanical Engineering Society卷 / 50期
关键词
Differential evolution; Parameters identification; Self-adaptive control parameters; Tire model;
D O I
10.3901/JME.2014.06.120
中图分类号
学科分类号
摘要
According to the situation that the fixed control parameters and evolutionary strategy of traditional differential evolution cannot be adapted to complex problems, a new self-adaptive differential evolution (NSADE) is presented, and further utilized to overcome the difficulty of identifying the parameters of magic formula (MF) tire model. The algorithm combines the selection strategy of control parameters based on the successfully evolutionary individuals and the evolutionary strategy based on dual trial vectors to achieve the effective self-adaptation of control parameters. By the identification of lateral force parameters and aligning moment parameters for pure slip, the new algorithm is demonstrated to have more powerful ability of global optimization and fast convergence than the other two advanced self-adaptive differential evolution, IMMa optimization algorithm (IOA) and differential evolution with self-adapting strategy and control parameters (SspDE), and the identified results are more accurate than that of the traditional Levenberg-Marquardt method, which indicates NSADE is an efficient method to identify the parameters of MF tire model. © 2014 Journal of Mechanical Engineering.
引用
收藏
页码:120 / 128
页数:8
相关论文
共 13 条
  • [1] Pacejka H.B., Sharp R.S., Shear force development by pneumatic tyre in steady state conditions: A review of modelling aspects, Vehicle System Dynamics, 20, 3-4, pp. 121-176, (1991)
  • [2] van Oosten J.J., Bakker E., Determination of magic tyre model parameters, Vehicle System Dynamics, 21, pp. 19-29, (2004)
  • [3] Holland J.H., Genetic Algorithms and the optimal allocations of trials, SIAM Journal of Computing, 2, 2, pp. 88-105, (1973)
  • [4] Vetturi D., Gadola M., Manzo L., Et al., Genetic Algorithm for tyre model identification in automotive dynamics studies, The 29th ISATA2 International Symposium on Automotive Technology and Automation, pp. 24-31, (1996)
  • [5] Cabrera J.A., Ortiz A., Carabias E., Et al., An alternative method to determine the magic tyre model parameters using genetic algorithms, Vehicle System Dynamics, 41, 2, pp. 109-127, (2004)
  • [6] Storn R., Price K., Differential evolution - A simple and efficient heuristic scheme for global optimization over continuos spaces, Journal of Global Optimization, 11, 4, pp. 341-359, (1997)
  • [7] Ortiz J.A., Cabrera A.J., Guerra A., Et al., An easy procedure to determine magic formula parameters: A comparative study between the starting value optimization technique and the IMMa optimization algorithm, Vehicle System Dynamics, 44, 9, pp. 689-718, (2006)
  • [8] Brest J., Greiner S., Boskovic B., Et al., Self-adapting control parameters in differential evolution: A compar-ative study on numerical benchmark problems, IEEE Transaction on Evolutionary Computation, 10, 6, pp. 646-657, (2006)
  • [9] Qin A.K., Huang V.L., Suganthan P.N., Differential evolution algorithm with strategy adaptation for global numerical optimization, IEEE Transactions on Evolutionary Computations, 13, 2, pp. 398-417, (2009)
  • [10] Pan Q.K., Suganthan P.N., Wang L., Et al., A differential evolution algorithm with self-adapting strategy and control parameters, Computers Operations Research, 38, pp. 394-408, (2011)