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arXiv · 2509.01461

A constrained optimization approach to nonlinear system identification through simulation error minimization

Vito Cerone·Sophie M. Fosson·Simone Pirrera·Diego Regruto
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Abstract

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.

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Vito Cerone, Sophie M. Fosson, Simone Pirrera, Diego Regruto. 2025-09-01. A constrained optimization approach to nonlinear system identification through simulation error minimization. https://arxiv.org/abs/2509.01461

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