State Estimation of Suspension System Based on Interacting Multiple Model Unscented Kalman Filter

被引：0
|
作者：
Wang Z. [1 ,2 ]
Li F. [1 ,2 ]
Wang X. [1 ,2 ]
Yang J. [1 ,2 ]
Qin Y. [3 ]
机构：
[1] Automotive Engineering Research Institute, China Automotive Technology and Research Center Co., Ltd., Tianjin
[2] CATARC (Tianjin) Automotive Engineering Research Institute Co., Ltd., Tianjin
[3] School of Mechanical Engineering, Beijing Institute of Technology, Beijing
来源：
Qin, Yechen (qinyechen@bit.edu.cn) | 1600年 / China Ordnance Industry Corporation卷 / 42期
关键词：
Interacting multiple model; Markov chain matrix; Monte Carlo; State estimation; Suspension system; Unscented Kalman filter;
D O I：
10.3969/j.issn.1000-1093.2021.02.003
中图分类号：
学科分类号：
摘要：
The accuracy estimation of suspension state under the conditions of time-varying road excitation and model parameter uncertainty is realized to effectively solve the issue that the state estimation of the nonlinear suspension system cannot be accurately achieved under complex driving conditions. The state estimation of suspension system is studied. Based on the models of road profile excitation and nonlinear suspension system, a novel interacting multiple model unscented Kalman filter (IMMUKF) algorithm is designed using the interacting multiple model algorithm and Markovchain Monte Carlo theory. IMMUKF algorithm is used to estimate the movement state of suspension system under various working conditions. The stability conditions of the proposed algorithm is validated using the stochastic stability theory. The accuracy of the nonlinear suspension movement state was estimated in real-time by comparing the traditional unscented Kalman filter (UKF) algorithm with the proposed IMMUKF algorithm under the various road inputs, and the suspension system was tested and verified. Experimental and simulated results show that the higher accuracy of the proposed algorithm can be obtained, and the maximum root mean square error of state estimation of the proposed algorithm in simulation is less than 8%. © 2021, Editorial Board of Acta Armamentarii. All right reserved.
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页码：242 / 253
页数：11
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