卡尔曼滤波器与神经网络串行的轮胎载荷识别模型

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Abstract Tire load is the basic data of vehicle design and safety evaluation. It is of great significance to identify tire load with high precision. In order to avoid the cost and complexity of direct measurement of tire load and the Limitations of load identification methods based on pure physical drive and pure data drive, A physical - data - driven load identification model is proposed. The model is composed of kalman filter and neural network correction model in series. Kalman filter is used to preliminarily identify the load，The modified model extracts the spatial and temporal characteristics of signals through the convolutional neural network and long and short term memory network, predicts the errors of kalman filter and corrects the recognition results. APM300 rubber wheel vehicle is taken as an example for load identification. The results show that the serial model can effectively restrain the influence of parameter perturbation and improve the identification accuracy of the model by organically combining physical drive and data drive methods and integrating the rules and experience of the whole system, and it has strong generalization performance and has certain engineering application value.

Key words： Kalman filter Convolutional neural network Long Short-Term Memory Combined data-driven and physical-driven method Tire load identification

Received: 20 March 2022 Published: 15 June 2023

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	Articles by authors
	ZENG Junwei
	JI Yuanjin
	REN Lihui
	ZHOU Rongsheng
	LI Chao
	YANG Xingrong

Cite this article:

ZENG Junwei,JI Yuanjin,REN Lihui, et al. A serial tire load identification model based on Kalman filter and neural network[J]. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(11): 262-270.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2023/V42/I11/262

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