A constrained optimization approach to nonlinear system identification through simulation error minimization
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909963860836352 |
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| author | Cerone, Vito Fosson, Sophie M. Pirrera, Simone Regruto, Diego |
| author_facet | Cerone, Vito Fosson, Sophie M. Pirrera, Simone Regruto, Diego |
| contents | This paper introduces a novel approach to system identification for nonlinear input-output models that minimizes the simulation error and frames the problem as a constrained optimization task. The proposed method addresses vanishing gradient issues, enabling faster convergence than traditional gradient-based techniques. We present an algorithm based on feedback linearization control of Lagrange multipliers and conduct a theoretical analysis of its performance. We prove that the algorithm converges to a local minimum, and it enhances computational efficiency by exploiting the problem's structure. Numerical experiments demonstrate that our approach outperforms gradient-based methods in both computational effort and estimation accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01461 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A constrained optimization approach to nonlinear system identification through simulation error minimization Cerone, Vito Fosson, Sophie M. Pirrera, Simone Regruto, Diego Optimization and Control Systems and Control This paper introduces a novel approach to system identification for nonlinear input-output models that minimizes the simulation error and frames the problem as a constrained optimization task. The proposed method addresses vanishing gradient issues, enabling faster convergence than traditional gradient-based techniques. We present an algorithm based on feedback linearization control of Lagrange multipliers and conduct a theoretical analysis of its performance. We prove that the algorithm converges to a local minimum, and it enhances computational efficiency by exploiting the problem's structure. Numerical experiments demonstrate that our approach outperforms gradient-based methods in both computational effort and estimation accuracy. |
| title | A constrained optimization approach to nonlinear system identification through simulation error minimization |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2509.01461 |