融合状态观测及优化方法的纯侧偏轮胎模型辨识

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Abstract Aiming at limits of most current tire model recognition being based on the assumption of tire force, tire sideslip angle etc. being known or measurable, a new method for tire pure sideslip model recognition based on the magic formula (MF) of on-board sensor and overall vehicle handling stability test was proposed. In this method, the state observation and optimization method were fused. Firstly, characteristic parameters of the tire model were preliminarily estimated using the unscented Kalman filter (UKF) state observation system, and then the parametric recognition was converted into an optimization problem. the UKF state observation system provided initial values for the particle swarm optimization (PSO) algorithm. UKF estimation results combined with parametric sensitivity analysis ones gained with MF of tire pure sideslip model to provide a search interval for PSO algorithm, and further obtain a more accurate recognition. The results of Simulink simulation and Simulink-Carsim joint simulation under different lateral accelerations showed that when lateral acceleration of a vehicle reaches a certain level to make tire enter nonlinear domain, the proposed recognition method can get more accurate recognition results.

Key words： Magic Formula tyre model parameter identification state estimation Unscented Kalman Filter Particle Swarm optimization

Received: 17 January 2019 Published: 28 June 2020

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	Articles by authors
	QIU Xiang1
	WU Xiaojian2
	ZHOU Bing3

Cite this article:

QIU Xiang1,WU Xiaojian2,ZHOU Bing3. Tire pure sideslip model recognition using fused state observation and optimization method[J]. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(13): 84-90.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2020/V39/I13/84

[1]    Pacejka H. Tire and vehicle dynamics (Third Edition) [M]. Elsevier, 2012.
[2]    Liu YH, Li T, Yang YY, et al., Estimation of tire-road friction coefficient based on combined APF-IEKF and iteration algorithm[J]. Mechanical Systems & Signal Processing. 2017, 88: 25-35.
[3]    田晶晶, 阳冬波, 李枭. 基于遗传算法的魔术公式轮胎模型参数辨识方法研究[J]. 交通节能与环保. 2014, 4: 13-16.
TIAN Jingjing, YANG Dongbo, LI Xiao. Study on the parameters identification method of magic formula tyre model based on genetic algorithm[J]. Energy Conservaton & Environmental Protection in Transportation,2014, 4: 13-16.
[4]    韩加蓬, 王艳阳. 基于遗传算法的LuGre轮胎模型参数辨识研究[J]. 数学的实践与认识. 2007, 37(18): 72-77.
HAN Jiapeng, WANG Yanyang. Research on the parameter identification of LuGre tire model based on Genetic algorithms[J]. Mathematics in Practice and Theory.,2007, 37(18): 72-77.
[5]    Tan D, Wang Y, Zhang L. Research on the parameter identification of LuGre tire model based on genetic algorithms[C]//International Conference on Intelligent Systems and Knowledge Engineering 2007. Atlantis Press, 2007.
[6]    Talebitooti R and Torabi M. Identification of tire force characteristics using a Hybrid method[J]. Applied Soft Computing. 2016,40: 70-85.
[7]    张家旭, 李静. 基于混合优化方法的UniTire轮胎模型参数辨识[J]. 吉林大学学报(工学版). 2017, 47(1): 15-20.
ZHANG Jiaxu, LI Jing. Parameter identification for UniTire model based on hybrid optimization method[J]. Journal of Jilin University( Engineering and Technology Edition),2017, 47(1): 15-20
[8]    Cabrera JA, Ortiz A, Carabias E and Simon A. An Alternative Method to Determine the Magic Tyre Model Parameters Using Genetic Algorithms[J]. Vehicle System Dynamics. 2004,41(2): 109-127.
[9]    张云清, 陈伟, 陈立平, 杨景周. Magic Formula轮胎模型参数辨识的一种混合优化方法[J]. 汽车工程. 2007, 29(3): 250-253.
ZHANG Yunqing, CHEN Wei, CHEN Liping, et al. A hybrid optimization on parameters identification for Magic Formula tire model[J]. Automotive Engineering, 2007, 29(3): 250-253.
[10] 王前, 杨志坚,丁康. 基于新自适应差分进化算法的Magic Formula轮胎模型参数辨识方法[J]. 机械工程学报. 2014,50(6): 120-128.
WANG Qian, YANG Zhijian, DING Kang. Method in identifing the parameters of Magic Formula tire model based on new self-adaptive differential evolution[J]. Journal of Mechanical Engineering,2014,50(6): 120-128.
[11] Baffet G., Charara A., Lechner D., Estimation of vehicle sideslip, tire force and wheel cornering stiffness. Control Engineering Practice[J].2009,17(11): 1255-1264.
[12] 李玲, 马力, 牟宇, 等. 模型车轮胎侧偏刚度的参数辨识方法[J]. 汽车工程. 2016,38(12): 1508-1514.
LI Ling, MA Li, MU Yu, et al,. Parameter identification method for the tire cornering stiffness of model vehicle[J]. Automotive Engineering,2016,38(12): 1508-1514.
[13] Sierra C., Tseng E., Jain A., et al. Cornering stiffness estimation based on vehicle lateral dynamics[J]. Vehicle System Dynamics. 2006, 44(sup 1): 24-38.
[14] Braghin F, Cheli F and Sabbioni E. Identification of Tire Model Parameters Through Full Vehicle Experimental Tests[J]. Journal of Dynamic Systems Measurement & Control. 2011, 133(3): 2049-2053.
[15]西蒙著, 张勇刚, 李宁, 等译. 最优状态估计:卡尔曼,H∞及非线性滤波[M]. 北京:国防工业出版社, 2013.
[16] 管军, 周家胜, 易文俊, 等. 基于自适应混沌变异粒子群优化算法的旋转弹丸气动参数辨识[J]. 兵工学报. 2017, 38(1): 73-80.
GUAN Jun, ZHOU Jiasheng, YI Wenjun, et al,. Identification of spinning projectile aerodynamic parameters using adaptive chaotic mutation particle swarm optimization[J]. Acta Armamentarii, 2017, 38(1): 73-80.
[17] 吴晓建, 周兵, 文桂林. 液压互联悬架抗侧倾控制研究[J]. 中国公路学报. 2018, 31(3):123-132.
WU Xiaojian, ZHOU Bing, WEN Guilin. Hydraulically interconnected suspension antiroll control research[J]. China Journal of Highway and Transport, 2018, 31(3): 123-132.