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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.11279 |
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| _version_ | 1866914419456344064 |
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| author | Li, Jiachen Li, Shihao Chen, Dongmei |
| author_facet | Li, Jiachen Li, Shihao Chen, Dongmei |
| contents | This paper introduces Smart Predict Then Control (SPC), a control aware refinement procedure for model based control. SPC refines a prediction oriented model by optimizing a surrogate objective that evaluates candidate models through the control actions they induce. For a fixed surrogate variant under unconstrained control, we establish the smoothness of the surrogate, projected gradient convergence at a sublinear rate of order one over K, and a bias decomposition that yields a conditional transfer diagnostic. On a wind disturbed quadrotor trajectory tracking task, Updated SPC reduces tracking RMSE by 70 percent and closed loop cost by 42 percent relative to the nominal baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11279 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Smart Predict-Then-Control: Control-Aware Surrogate Refinement for System Identification Li, Jiachen Li, Shihao Chen, Dongmei Systems and Control This paper introduces Smart Predict Then Control (SPC), a control aware refinement procedure for model based control. SPC refines a prediction oriented model by optimizing a surrogate objective that evaluates candidate models through the control actions they induce. For a fixed surrogate variant under unconstrained control, we establish the smoothness of the surrogate, projected gradient convergence at a sublinear rate of order one over K, and a bias decomposition that yields a conditional transfer diagnostic. On a wind disturbed quadrotor trajectory tracking task, Updated SPC reduces tracking RMSE by 70 percent and closed loop cost by 42 percent relative to the nominal baseline. |
| title | Smart Predict-Then-Control: Control-Aware Surrogate Refinement for System Identification |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2506.11279 |