Local Sensitivity Analysis for Kernel-Regularized ARX Predictors in Data-Driven Predictive Control
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911573520416768 |
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| author | Liu, Aihui Jansson, Magnus |
| author_facet | Liu, Aihui Jansson, Magnus |
| contents | We study local sensitivity of structured ARX-based data-driven predictive control. Although predictor estimation is linear in the ARX parameters, the lifted multi-step predictor used in MPC depends on them implicitly, which complicates both uncertainty propagation and task-aware regularization. We derive a local first-order linearization of this implicit predictor map. The resulting Jacobian yields both an approximate control-relevant prediction uncertainty term and a task-dependent sensitivity metric for shaping kernel regularization. Numerical results show that the proposed analysis is most useful in weak-excitation regimes, where baseline SS regularization already provides substantial robustness gains and the proposed sensitivity shaping yields a further smaller improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05832 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Local Sensitivity Analysis for Kernel-Regularized ARX Predictors in Data-Driven Predictive Control Liu, Aihui Jansson, Magnus Systems and Control We study local sensitivity of structured ARX-based data-driven predictive control. Although predictor estimation is linear in the ARX parameters, the lifted multi-step predictor used in MPC depends on them implicitly, which complicates both uncertainty propagation and task-aware regularization. We derive a local first-order linearization of this implicit predictor map. The resulting Jacobian yields both an approximate control-relevant prediction uncertainty term and a task-dependent sensitivity metric for shaping kernel regularization. Numerical results show that the proposed analysis is most useful in weak-excitation regimes, where baseline SS regularization already provides substantial robustness gains and the proposed sensitivity shaping yields a further smaller improvement. |
| title | Local Sensitivity Analysis for Kernel-Regularized ARX Predictors in Data-Driven Predictive Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2604.05832 |