| Autor: |
Qiangqiang Li, Zhiyong Chen, Wenku Shi |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Actuators, Vol 12, Iss 2, p 70 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2076-0825 |
| DOI: |
10.3390/act12020070 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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