Learning Based MPC for Autonomous Driving Using a Low Dimensional Residual Model
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915049333850112 |
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| author | Li, Yaoyu Huang, Chaosheng Yang, Dongsheng Liu, Wenbo Li, Jun |
| author_facet | Li, Yaoyu Huang, Chaosheng Yang, Dongsheng Liu, Wenbo Li, Jun |
| contents | In this paper, a learning based Model Predictive Control (MPC) using a low dimensional residual model is proposed for autonomous driving. One of the critical challenge in autonomous driving is the complexity of vehicle dynamics, which impedes the formulation of accurate vehicle model. Inaccurate vehicle model can significantly impact the performance of MPC controller. To address this issue, this paper decomposes the nominal vehicle model into invariable and variable elements. The accuracy of invariable component is ensured by calibration, while the deviations in the variable elements are learned by a low-dimensional residual model. The features of residual model are selected as the physical variables most correlated with nominal model errors. Physical constraints among these features are formulated to explicitly define the valid region within the feature space. The formulated model and constraints are incorporated into the MPC framework and validated through both simulation and real vehicle experiments. The results indicate that the proposed method significantly enhances the model accuracy and controller performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03874 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Based MPC for Autonomous Driving Using a Low Dimensional Residual Model Li, Yaoyu Huang, Chaosheng Yang, Dongsheng Liu, Wenbo Li, Jun Robotics Systems and Control In this paper, a learning based Model Predictive Control (MPC) using a low dimensional residual model is proposed for autonomous driving. One of the critical challenge in autonomous driving is the complexity of vehicle dynamics, which impedes the formulation of accurate vehicle model. Inaccurate vehicle model can significantly impact the performance of MPC controller. To address this issue, this paper decomposes the nominal vehicle model into invariable and variable elements. The accuracy of invariable component is ensured by calibration, while the deviations in the variable elements are learned by a low-dimensional residual model. The features of residual model are selected as the physical variables most correlated with nominal model errors. Physical constraints among these features are formulated to explicitly define the valid region within the feature space. The formulated model and constraints are incorporated into the MPC framework and validated through both simulation and real vehicle experiments. The results indicate that the proposed method significantly enhances the model accuracy and controller performance. |
| title | Learning Based MPC for Autonomous Driving Using a Low Dimensional Residual Model |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2412.03874 |