| Autor: |
Mattia Piccinini, Sebastiano Taddei, Matteo Larcher, Mattia Piazza, Francesco Biral |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 11, Pp 46344-46372 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3274836 |
| Popis: |
This paper presents a hierarchical framework with novel analytical and neural physics-driven models, to enable the online planning and tracking of minimum-time maneuvers, for a vehicle with partially-unknown parameters. We introduce a lateral speed prediction model for high-level motion planning with economic nonlinear model predictive control (E-NMPC). A low-level steering controller is developed with a novel feedforward-feedback physics-driven artificial neural network (NN). A longitudinal dynamic model is identified to tune a low-level speed-tracking controller. The high- and low-level control models are identified with an automatic three-step scheme, combining open-loop and closed-loop maneuvers to model the maximum acceleration G-G-v performance constraint for E-NMPC, and to capture the effect of the longitudinal acceleration on the lateral dynamics. The proposed framework is used in a simulation environment, for the online closed-loop control of a highly detailed sedan vehicle simulator, whose parameters are partially-unknown. Two different circuits are adopted to validate the approach, and a robustness analysis is performed by varying the vehicle mass and the load distribution. A minimum-time optimal control problem is solved offline and used for a comparison with the closed-loop results. A video demonstrating both the automatic three-step identification scheme and the motion planning and control results is available at the following link: https://www.youtube.com/watch?v=xQ_T96IjGP8. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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