A Particle Filter Approach for Identifying Tire Model Parameters From Full-Scale Experimental Tests

被引：10

作者：

Sabbioni, Edoardo ^{[1
]}

Bao, Ruixin ^{[2
]}

Cheli, Federico ^{[1
]}

Tarsitano, Davide ^{[1
]}

机构：

[1] Politecn Milan, Dept Mech Engn, Via La Masa 1, I-20156 Milan, Italy

[2] Liaoning Shihua Univ, Liaoning Univ, Sch Mech Engn, Fushun 113001, Peoples R China

来源：

JOURNAL OF MECHANICAL DESIGN | 2017年 / 139卷 / 02期

关键词：

STATE ESTIMATION;

D O I：

10.1115/1.4035186

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Mathematical models simulating the handling behavior of passenger cars are extensively used at a design stage for evaluating the effects of new structural solutions or control systems. The main source of uncertainty in these type of models lies in tire-road interaction, due to high nonlinearity. Proper estimation of tire model parameters is thus of utter importance to obtain reliable results. This paper presents a methodology aimed at identifying the magic formula-tire (MF-Tire) model coefficients of the tires of an axle only based on measurements carried out on board vehicle (vehicle sideslip angle, yaw rate, lateral acceleration, speed, and steer angle) during standard handling maneuvers (step-steers, double lane changes, etc.). The proposed methodology is based on particle filtering (PF) technique. PF may become a serious alternative to classic model-based techniques, such as Kalman filters. Results of the identification procedure were first checked through simulations. Then, PF was applied to experimental data collected using an instrumented passenger car.

引用

页数：7

共 21 条

[11] An overview of existing methods and recent advances in sequential Monte Carlo [J].

Cappe, Olivier ;

Godsill, Simon J. ;

Moulines, Eric .

PROCEEDINGS OF THE IEEE, 2007, 95 (05) :899-924

[12] Parameter and state estimation for articulated heavy vehicles [J].

Cheng, Caizhen ;

Cebon, David .

VEHICLE SYSTEM DYNAMICS, 2011, 49 (1-2) :399-418

[13]

Doucet A, 1998, Tech. Rep. CUED/F-INFENG/TR.310

[14] A particle filtering and kernel smoothing-based approach for new design component prognostics [J].

Hu, Yang ;

Baraldi, Piero ;

Di Maio, Francesco ;

Zio, Enrico .

RELIABILITY ENGINEERING & SYSTEM SAFETY, 2015, 134 :19-31

[15]

[林棻 Lin Fen], 2011, [农业机械学报, Transactions of the Chinese Society of Agricultural Machinery], V42, P23

[16] A particle-filtering approach for on-line fault diagnosis and failure prognosis [J].

Orchard, Marcos E. ;

Vachtsevanos, George J. .

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2009, 31 (3-4) :221-246

[17]

Pacejka H, 2002, TIRE VEHICLE DYNAMIC

[18]

Pacejka H., 1992, VEHICLE SYSTEM DYNAM, V21, P1, DOI DOI 10.1080/00423119208969994

[19]

Rekleitis I. M., 2002, TRCIM0402 MCGILL U

[20]

Robert Christian P, 1999, Monte Carlo statistical methods, V2

← 1 2 3 →