Multi-Body Model Identification of Vehicle Semi-Active Suspension Based on Genetic Neural Network

Jing Jun Zhang; Bing An Han; Rui Zen Gao

doi:10.4028/www.scientific.net/AMM.121-126.4069

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 121-126Multi-Body Model Identification of Vehicle...

Multi-Body Model Identification of Vehicle Semi-Active Suspension Based on Genetic Neural Network

Abstract:

A multi-body vehicle dynamics model was established using ADAMS and a multilayer feed forward neural network of series parallel structure was built by Matlab in this study. The weights and threshold of neural networks which has built was optimizes by GA. This method was used in identifying multi-body vehicle dynamics model. The results show that the maximum error of identification is less than 0.05% and the network convergence rapidly. The designed genetic neural network could replace the vehicle semi-active suspension systems using in neural network adaptive control which can avoid the difficulty of establishing accurately mathematical model and the poor effective of traditional identification methods for the vehicle semi-active suspension.

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Periodical:

Applied Mechanics and Materials (Volumes 121-126)

Pages:

4069-4073

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.121-126.4069

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Online since:

October 2011

Authors:

Jing Jun Zhang, Bing An Han, Rui Zen Gao

Keywords:

Genetic Neural Networks, Identification, Semi-Active Suspension

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References

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