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基于IMMAKF在可变车速、道路粗糙度等级和簧载质量条件下的自适应悬架状态估计。

Adaptive suspension state estimation based on IMMAKF on variable vehicle speed, road roughness grade and sprung mass condition.

作者信息

Wu Xiao, Shi Wenku, Zhang Hong, Chen Zhiyong

机构信息

State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, China.

Weifang Economic School, Zhucheng, China.

出版信息

Sci Rep. 2024 Jan 19;14(1):1740. doi: 10.1038/s41598-023-49766-y.

DOI:10.1038/s41598-023-49766-y
PMID:38242889
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10799087/
Abstract

Vehicle speed, road roughness grade and sprung mass are the three main factors to influence suspension control and state estimation. Aiming at the problem that fixed state observer cannot guarantee the estimation accuracy of suspension with driving scenario changes, a suspension state observer based on interactive multiple model adaptive Kalman filter (IMMAKF) is established. Firstly, an adaptive control suspension is proposed based on LQR algorithm and multi-objective optimization algorithm, which can automatically adjust the controller parameters according to the vehicle speed, road roughness grade and sprung acceleration parameters, so as to keep the optimal control effect of the suspension. Secondly, the theoretical model of IMMAKF is derived, and two kinds of IMMAKF suspension state observers and controllers are established. Finally, a simulation condition with the vehicle speed, road roughness grade and sprung mass changing simultaneously is established. The simulation results shows that: compared with ordinary IMMKF, AKF and KF observers, the estimation accuracy of IMMAKF5 is improved. Except for state observation, IMMAKF can be used to identify the road roughness grade and estimate the suspension sprung mass.

摘要

车速、路面不平度等级和簧载质量是影响悬架控制和状态估计的三个主要因素。针对固定状态观测器无法随驾驶场景变化保证悬架估计精度的问题，建立了一种基于交互式多模型自适应卡尔曼滤波器（IMMAKF）的悬架状态观测器。首先，提出了一种基于线性二次型调节器（LQR）算法和多目标优化算法的自适应控制悬架，其可根据车速、路面不平度等级和簧载加速度参数自动调整控制器参数，以保持悬架的最优控制效果。其次，推导了IMMAKF的理论模型，并建立了两种IMMAKF悬架状态观测器和控制器。最后，建立了车速、路面不平度等级和簧载质量同时变化的仿真工况。仿真结果表明：与普通交互式多模型卡尔曼滤波器（IMMKF）、自适应卡尔曼滤波器（AKF）和卡尔曼滤波器（KF）观测器相比，IMMAKF5的估计精度有所提高。除状态观测外，IMMAKF还可用于识别路面不平度等级和估计悬架簧载质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c001/10799087/263174a4cc84/41598_2023_49766_Fig11_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c001/10799087/8c0529c09375/41598_2023_49766_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c001/10799087/dd3c5d911282/41598_2023_49766_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c001/10799087/9848d5d96b08/41598_2023_49766_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c001/10799087/495546f97b75/41598_2023_49766_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c001/10799087/04a93bb5273b/41598_2023_49766_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c001/10799087/a129eb43360c/41598_2023_49766_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c001/10799087/3e5d1a970a0b/41598_2023_49766_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c001/10799087/263174a4cc84/41598_2023_49766_Fig11_HTML.jpg

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