Vehicle suspension vibration control using recurrent neural networks

Author

Huawei Guan ; Xinyi Le ; Jun Wang

Author_Institution

Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Hong Kong, China

fYear

2014

fDate

18-20 Aug. 2014

Firstpage

435

Lastpage

440

Abstract

This paper presents an application of vibration control to a half-car model using recurrent neural networks. The robust vibration control is formulated as equality constrained optimization problem. Simulation results show that the close-loop system has good response performance in the presence of disturbances generated by an isolated bump. The study shows potential in using neural networks for the active vibration control in precision machine design.

Keywords

automobiles; closed loop systems; design engineering; mechanical engineering computing; optimisation; recurrent neural nets; robust control; suspensions (mechanical components); vehicle dynamics; vibration control; close-loop system; equality constrained optimization problem; half-car model; isolated bump; precision machine design; recurrent neural networks; robust vibration control; vehicle suspension vibration control; Mathematical model; Neurodynamics; Optimization; Recurrent neural networks; Suspensions; Vibration control; Vibrations;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Information Processing (ICICIP), 2014 Fifth International Conference on

Conference_Location

Dalian

Print_ISBN

978-1-4799-3649-6

Type

conf

DOI

10.1109/ICICIP.2014.7010293

Filename

7010293