Title :
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
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;
Conference_Titel :
Intelligent Control and Information Processing (ICICIP), 2014 Fifth International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4799-3649-6
DOI :
10.1109/ICICIP.2014.7010293
