Tire Material Parameters Identification of Mining Dump Truck Based on EKF-BP

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Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (17) : 71-76.

Zhang Feifei 1 Gu Zhengqi 1,2 Zhang Sha1 Ma Xiaokui1 Zhu Yifan1

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Abstract

Tire material parameters on tire finite element model is very important, but the tire material is more and the structure is complex,leading to the tire material parameters are difficult to obtain, the method of using BP neural network which is optimized by Extend Kalman filter to identify the tire parameters is proposed. The dynamic simulation of the tire pulse condition is simulated based on the tire finite element model, The tire acceleration that is obtained by simulating is regarded as an ideal input sample of neural network,and the tire material parameters that is need to identify is regarded as an ideal output sample of neural network, then the nonlinear mapping network model between them is built by network training. The tire vertical acceleration test data which are denoised by wavelet input trained network, can effectively identify tire material parameters. Through the comparison of simulation data under corresponding conditions and the experimental data, show that the biggest error is 6.45%, it is proved that the accuracy of the vertical characteristics of tire finite element model based on the identification of material parameters.

Key words

Mining dump truck tire / Parameter identification / BP neural network / Extend Kalman filter / Wavelet denoise

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Zhang Feifei 1 Gu Zhengqi 1,2 Zhang Sha1 Ma Xiaokui1 Zhu Yifan1 . Tire Material Parameters Identification of Mining Dump Truck Based on EKF-BP[J]. Journal of Vibration and Shock, 2016, 35(17): 71-76

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