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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Actuators |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-0825/12/2/70 |
| _version_ | 1797623065051398144 |
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| author | Qiangqiang Li Zhiyong Chen Wenku Shi |
| author_facet | Qiangqiang Li Zhiyong Chen Wenku Shi |
| author_sort | Qiangqiang Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T09:20:13Z |
| format | Article |
| id | doaj.art-9e8488b07631490ca520c6549f6b6d74 |
| institution | Directory Open Access Journal |
| issn | 2076-0825 |
| language | English |
| last_indexed | 2024-03-11T09:20:13Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj.art-9e8488b07631490ca520c6549f6b6d742023-11-16T18:25:29ZengMDPI AGActuators2076-08252023-02-011227010.3390/act12020070A Novel State Estimation Approach for Suspension System with Time-Varying and Unknown Noise CovarianceQiangqiang Li0Zhiyong Chen1Wenku Shi2The State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130000, ChinaThe State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130000, ChinaThe State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130000, ChinaIn 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.https://www.mdpi.com/2076-0825/12/2/70suspension systemvariational Bayesianadaptive Kalman filterinverse Wishart distributionroad classification |
| spellingShingle | Qiangqiang Li Zhiyong Chen Wenku Shi A Novel State Estimation Approach for Suspension System with Time-Varying and Unknown Noise Covariance Actuators suspension system variational Bayesian adaptive Kalman filter inverse Wishart distribution road classification |
| title | A Novel State Estimation Approach for Suspension System with Time-Varying and Unknown Noise Covariance |
| title_full | A Novel State Estimation Approach for Suspension System with Time-Varying and Unknown Noise Covariance |
| title_fullStr | A Novel State Estimation Approach for Suspension System with Time-Varying and Unknown Noise Covariance |
| title_full_unstemmed | A Novel State Estimation Approach for Suspension System with Time-Varying and Unknown Noise Covariance |
| title_short | A Novel State Estimation Approach for Suspension System with Time-Varying and Unknown Noise Covariance |
| title_sort | novel state estimation approach for suspension system with time varying and unknown noise covariance |
| topic | suspension system variational Bayesian adaptive Kalman filter inverse Wishart distribution road classification |
| url | https://www.mdpi.com/2076-0825/12/2/70 |
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