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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.06313 |
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| _version_ | 1866929414359482368 |
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| author | Kopp, Fionna B. Borrelli, Francesco |
| author_facet | Kopp, Fionna B. Borrelli, Francesco |
| contents | We present a Learning Model Predictive Controller (LMPC) for multi-modal systems performing iterative control tasks. Assuming availability of historical data, our goal is to design a data-driven control policy for the multi-modal system where the current mode is unknown. First, we propose a novel method to select local data for constructing affine time-varying (ATV) models of a multi-modal system in the context of LMPC. Then we present how to build a sampled safe set from multi-modal historical data. We demonstrate the effectiveness of our method through simulation results of automated driving on a friction-varying track. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06313 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Driven Multi-Modal Learning Model Predictive Control Kopp, Fionna B. Borrelli, Francesco Systems and Control We present a Learning Model Predictive Controller (LMPC) for multi-modal systems performing iterative control tasks. Assuming availability of historical data, our goal is to design a data-driven control policy for the multi-modal system where the current mode is unknown. First, we propose a novel method to select local data for constructing affine time-varying (ATV) models of a multi-modal system in the context of LMPC. Then we present how to build a sampled safe set from multi-modal historical data. We demonstrate the effectiveness of our method through simulation results of automated driving on a friction-varying track. |
| title | Data-Driven Multi-Modal Learning Model Predictive Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2407.06313 |