A Novel State Estimation Approach for Suspension System with Time-Varying and Unknown Noise Covariance

In this paper, a novel state estimation approach based on the variational Bayesian adaptive Kalman filter (VBAKF) and road classification is proposed for a suspension system with time-varying and unknown noise covariance. Using the VB approach, the time-varying noise covariance can be inferred from...

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Bibliographic Details
Published in:Actuators 2023-02, Vol.12 (2), p.70
Main Authors: Li, Qiangqiang, Chen, Zhiyong, Shi, Wenku
Format: Article
Language:English
Subjects:
Accuracy
adaptive Kalman filter
Algorithms
Classification
Conditional probability
Correlation analysis
Covariance
Distribution (Probability theory)
Distribution functions
Fuzzy sets
inverse Wishart distribution
Kalman filters
Methods
Multiple objective analysis
Optimization
Probability distribution functions
road classification
State estimation
suspension system
Suspension systems
variational Bayesian
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Summary:In this paper, a novel state estimation approach based on the variational Bayesian adaptive Kalman filter (VBAKF) and road classification is proposed for a suspension system with time-varying and unknown noise covariance. Using the VB approach, the time-varying noise covariance can be inferred from the inverse-Wishart distribution and then optimized state estimation by the finite sampling posterior probability distribution function (PDF) of noise covariance and backward Kalman smoothing. In addition, a new road classification algorithm based on multi-objective optimization and the linear classifier is proposed to identify the unknown noise covariance. Simulation results for a suspension model with time-varying and unknown noise covariance show that the proposed approach has a higher performance in state estimation accuracy than other filters.
ISSN:2076-0825
2076-0825
DOI:10.3390/act12020070