Adaptive Control with Sparse Identification of Nonlinear Dynamics
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866911583772344320 |
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| author | Satharasi, Trivikram Ogri, Tochukwu E. Qureshi, Muzaffar Volle, Kyle Kamalapurkar, Rushikesh |
| author_facet | Satharasi, Trivikram Ogri, Tochukwu E. Qureshi, Muzaffar Volle, Kyle Kamalapurkar, Rushikesh |
| contents | This paper develops a sparsity-promoting integral concurrent learning (SP-ICL) adaptation law for a linearly parametrized uncertain nonlinear control-affine system. The unknown parameters are learned using ICL with sparsity-promoting $\ell_1$ regularization. The use of $\ell_1$ regularization for sparsity promotion is common in system identification and machine learning; however, unlike existing approaches, this paper develops an online parameter update law that integrates the regularization penalty with ICL via sliding modes. Using the SP-ICL update law, we show via non-smooth Lyapunov analysis that the trajectories of the closed-loop system are ultimately bounded. Simulations verify the effectiveness of the sparsity penalty in the SP-ICL update law on recovering sparse dynamics during trajectory tracking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06338 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adaptive Control with Sparse Identification of Nonlinear Dynamics Satharasi, Trivikram Ogri, Tochukwu E. Qureshi, Muzaffar Volle, Kyle Kamalapurkar, Rushikesh Optimization and Control Systems and Control 93D21, 93D05 This paper develops a sparsity-promoting integral concurrent learning (SP-ICL) adaptation law for a linearly parametrized uncertain nonlinear control-affine system. The unknown parameters are learned using ICL with sparsity-promoting $\ell_1$ regularization. The use of $\ell_1$ regularization for sparsity promotion is common in system identification and machine learning; however, unlike existing approaches, this paper develops an online parameter update law that integrates the regularization penalty with ICL via sliding modes. Using the SP-ICL update law, we show via non-smooth Lyapunov analysis that the trajectories of the closed-loop system are ultimately bounded. Simulations verify the effectiveness of the sparsity penalty in the SP-ICL update law on recovering sparse dynamics during trajectory tracking. |
| title | Adaptive Control with Sparse Identification of Nonlinear Dynamics |
| topic | Optimization and Control Systems and Control 93D21, 93D05 |
| url | https://arxiv.org/abs/2604.06338 |