Active Suspension Control Based on Interacting Multiple Model Kalman Filter

被引：0
|
作者：
Wu X. [1 ]
Shi W. [1 ]
Chen Z. [1 ]
机构：
[1] Jilin University, State Key Laboratory of Automotive Simulation and Control, Changchun
来源：
Qiche Gongcheng/Automotive Engineering | 2023年 / 45卷 / 07期
关键词：
fuzzy control; interacting multiple model Kalman filter; road grade recognition; state observation;
D O I：
10.19562/j.chinasae.qcgc.2023.07.011
中图分类号：
学科分类号：
摘要：
For the problem that it is difficult for fixed state observer to ensure the accuracy of road adaptive suspension state observation，the suspension state observer and controller is established on the basis of interactive multi-model Kalman filter（IMMKF）. Firstly，the road adaptive active suspension system is established based on the LQG algorithm and fuzzy control algorithm. Combined with harmonic superposition method，the A-B-D-C grade spatial domain road roughness model is generated as the input of the simulation system. Secondly，three kinds of IMMKF suspension state observer and controller are established taking the optimal LQG model of all grades of road as the sub-models. The simulation comparison shows that the observation accuracy of the 14-model IMMKF suspension state observer can be improved by 98.17% maximumly compared with the ordinary Kalman filter，and can be used to identify road grade，and the body acceleration of the adaptive active suspension controller based on the 14-model IMMKF is reduced by 75.99% compared with the passive suspension and 47.16% compared with the ordinary LQG active suspension，which verifies the superiority of the model. © 2023 SAE-China. All rights reserved.
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页码：1200 / 1211and1253
相关论文
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