Robust Iterative Learning for Collaborative Road Profile Estimation and Active Suspension Control in Connected Vehicles
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929686061252608 |
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| author | Modi, Harsh Hajidavalloo, Mohammad R Li, Zhaojian Zheng, Minghui |
| author_facet | Modi, Harsh Hajidavalloo, Mohammad R Li, Zhaojian Zheng, Minghui |
| contents | This paper presents the development of a new collaborative road profile estimation and active suspension control framework in connected vehicles, where participating vehicles iteratively refine the road profile estimation and enhance suspension control performance through an iterative learning scheme. Specifically, we develop a robust iterative learning approach to tackle the heterogeneity and model uncertainties in participating vehicles, which are important for practical implementations. In addition, the framework can be adopted as an add-on system to augment existing suspension control schemes. Comprehensive numerical studies are performed to evaluate and validate the proposed framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17643 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robust Iterative Learning for Collaborative Road Profile Estimation and Active Suspension Control in Connected Vehicles Modi, Harsh Hajidavalloo, Mohammad R Li, Zhaojian Zheng, Minghui Systems and Control This paper presents the development of a new collaborative road profile estimation and active suspension control framework in connected vehicles, where participating vehicles iteratively refine the road profile estimation and enhance suspension control performance through an iterative learning scheme. Specifically, we develop a robust iterative learning approach to tackle the heterogeneity and model uncertainties in participating vehicles, which are important for practical implementations. In addition, the framework can be adopted as an add-on system to augment existing suspension control schemes. Comprehensive numerical studies are performed to evaluate and validate the proposed framework. |
| title | Robust Iterative Learning for Collaborative Road Profile Estimation and Active Suspension Control in Connected Vehicles |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2407.17643 |