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

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...

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Bibliographic Details
Published in:Scientific reports 2024-01, Vol.14 (1), p.1740-1740, Article 1740
Main Authors: Wu, Xiao, Shi, Wenku, Zhang, Hong, Chen, Zhiyong
Format: Article
Language:English
Subjects:
639/166/988
639/705/258
Accuracy
Algorithms
Control theory
Controllers
Driving ability
Humanities and Social Sciences
multidisciplinary
Roads
Science
Science (multidisciplinary)
Sensors
Simulation
Speed limits
Vehicles
Working conditions
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-49766-y