Datamodel-Based Data Selection for Nonlinear Data-Enabled Predictive Control
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918407246446592 |
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| author | Li, Jiachen Li, Shihao Xu, Jiamin Bakshi, Soovadeep Chen, Dongmei |
| author_facet | Li, Jiachen Li, Shihao Xu, Jiamin Bakshi, Soovadeep Chen, Dongmei |
| contents | Data-Enabled Predictive Control (DeePC) has emerged as a powerful framework for controlling unknown systems directly from input-output data. For nonlinear systems, recent work has proposed selecting relevant subsets of data columns based on geometric proximity to the current operating point. However, such proximity-based selection ignores the control objective: different reference trajectories may benefit from different data even at the same operating point. In this paper, we propose a datamodel-based approach that learns a context-dependent influence function mapping the current initial trajectory and reference trajectory to column importance scores. Adapting the linear datamodel framework from machine learning, we model closed-loop cost as a linear function of column inclusion indicators, with coefficients that depend on the control context. Training on closed-loop simulations, our method captures which data columns actually improve tracking performance for specific control tasks. Experimental results demonstrate that task-aware selection substantially outperforms geometry-based heuristics, particularly when using small data subsets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00276 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Datamodel-Based Data Selection for Nonlinear Data-Enabled Predictive Control Li, Jiachen Li, Shihao Xu, Jiamin Bakshi, Soovadeep Chen, Dongmei Systems and Control Data-Enabled Predictive Control (DeePC) has emerged as a powerful framework for controlling unknown systems directly from input-output data. For nonlinear systems, recent work has proposed selecting relevant subsets of data columns based on geometric proximity to the current operating point. However, such proximity-based selection ignores the control objective: different reference trajectories may benefit from different data even at the same operating point. In this paper, we propose a datamodel-based approach that learns a context-dependent influence function mapping the current initial trajectory and reference trajectory to column importance scores. Adapting the linear datamodel framework from machine learning, we model closed-loop cost as a linear function of column inclusion indicators, with coefficients that depend on the control context. Training on closed-loop simulations, our method captures which data columns actually improve tracking performance for specific control tasks. Experimental results demonstrate that task-aware selection substantially outperforms geometry-based heuristics, particularly when using small data subsets. |
| title | Datamodel-Based Data Selection for Nonlinear Data-Enabled Predictive Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.00276 |