Comfort and safety analysis of automotive active suspension based on adaptive filter theory

Author

Sun, Jiammin ; Yang, Qingmei

Author_Institution

Sch. of Mech.-Electron. & Automobile Eng., Beijing Univ. of Civil Eng. & Archit., Beijing

fYear

2009

fDate

25-27 May 2009

Firstpage

1730

Lastpage

1734

Abstract

The Least Means Squares (LMS) adaptive theory is used in active suspension system. With adjusting the weight of adaptive filter, the minimum quadratic performance index is obtained. For two-DOF automotive suspension model, LMS adaptive controller is studied. According to the evaluation method of riding comfort and handling safety of automotive, the acceleration of the sprung mass, dynamic tyre load between wheels and road and dynamic deflection between the sprung mass and the unsprung mass are determined as the evaluation targets of suspension performance. The simulation results show that LMS adaptive control is not only simple algorithm but also remarkably effective. The calculation of LMS adaptive control algorithm is much little, and the method is fit for the active control of the suspension system.

Keywords

adaptive control; adaptive filters; automotive components; least mean squares methods; road safety; suspensions (mechanical components); wheels; active control; adaptive controller; adaptive filter theory; automotive active suspension; comfort analysis; dynamic deflection; least means squares adaptive theory; minimum quadratic performance index; safety analysis; wheels; Acceleration; Adaptive control; Adaptive filters; Automotive engineering; Least squares approximation; Performance analysis; Programmable control; Road safety; Tires; Vehicle dynamics; active suspension; adaptive filter theory; comfort; safety;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics and Applications, 2009. ICIEA 2009. 4th IEEE Conference on

Conference_Location

Xi´an

Print_ISBN

978-1-4244-2799-4

Electronic_ISBN

978-1-4244-2800-7

Type

conf

DOI

10.1109/ICIEA.2009.5138493

Filename

5138493