Earticle

초록

영어

Elman neural network was utilized to accomplish mapping from vibrant acceleration space of unsprung mass to dynamic load space in order to identifying results of neural network can more approach dynamic changing course of air suspension during active controlling process of air suspension. The dynamic model of 1/4 engineering vehicle was established. Vibrant acceleration data and dynamic load data were got by simulation based on the dynamic model of 1/4 engineering vehicle. Vibrant acceleration data were selected to be input data and dynamic load data were selected to be output of Elman neural network. Elman neural network was trained by input and output data. Then, generalization of trained Elman neural network was tested as follows. Sine wave was selected as road input. When amplitude was selected as 0.1m and frequency was selected as 1 rad/s, data of identifying error rate within 30％ took 75.97％ in total data. When amplitude was selected as 0.05m and frequency was selected as 0.5 rad/s, data of identifying error rate within 30％ took 96.1％ in total data. Results indicated that Elman neural network possess better fitting ability on this situation. When amplitude was selected as 0.3m and frequency was selected as 2 rad/s, results indicated that identifying error rate decreased and identifying curve obviously separated with numerical curve. It is perhaps for the reason that more identifying data scale out the value boundary of trained data. Meanwhile identifying curve and numerical curve gradually approached and stabilized eventually. It demonstrated that Elman neural network can effectively approach dynamic changing course of air suspension.

목차

Abstract
 1. Introduction
 2. Working Principle of Air Spring
 3. Air Suspension Model of 1/4 Vehicle
 4. Identification based on Elman Neural Network
 5. Identifying Dynamic Load
 6. Conclusions
 References

키워드

저자

• Bin Yang [ School of Automotive and Transportation, Tianjin University of Technology and Education Tianjin, China / R & D Center of Great Wall Motor Company Limited, China / Hebei Province Automotive Engineering Technology Research Center, China ]
• Yongjun Li [ R & D Center of Great Wall Motor Company Limited, China / Hebei Province Automotive Engineering Technology Research Center, China ]
• Ningwu Wang [ R & D Center of Great Wall Motor Company Limited, China / Hebei Province Automotive Engineering Technology Research Center, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605