基于GA-LSTM自适应卡尔曼滤波的路面不平度识别

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Abstract Accurately and rapidly identifying the current road excitation information of the vehicle is the key to realize the intelligent chassis control and ensure the ride comfort of the vehicle. Aiming at the problems of low accuracy and poor adaptability of traditional road roughness recognition algorithms, a road roughness recognition algorithm based on genetic algorithm(GA)optimized long short-term memory neural network(LSTM)adaptive Kalman filtering was proposed. Based on the two-degree-of-freedom vehicle suspension model, the feature input variables of the LSTM neural network were selected by the grey correlation method, and the model parameters of the LSTM neural network were optimized by GA to accurately identify the road grade. Based on this, the noise matrix in the Kalman filtering algorithm is updated in real time, and the adaptive recognition of road roughness under complex road conditions is realized. Simulation and experimental results show that the proposed adaptive Kalman filtering algorithm with GA-LSTM can quickly and accurately identify road roughness and road grade. Compared with the traditional Kalman filtering algorithm, The correlation coefficient, root mean square error and maximum absolute error are increased by 3.11 %, 37.5 % and 51.2 %, respectively, indicating that the proposed algorithm has good adaptability to complex working conditions.

Key words： road roughness identification adaptive Kalman filtering GA-LSTM grey relational analysis

Received: 04 July 2023 Published: 15 May 2024

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	Articles by authors
	LI Shaohua1
	LI Jianwei1
	2
	FENG Guizhen2

Cite this article:

LI Shaohua1,LI Jianwei1,2, et al. Road roughness recognition based on GA-LSTM adaptive Kalman filtering[J]. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(9): 121-130.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2024/V43/I9/121

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