{"ID":2852184,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.24757","arxiv_id":"2510.24757","title":"Stable-by-Design Neural Network-Based LPV State-Space Models for System Identification","abstract":"Accurate modeling of nonlinear systems is essential for reliable control, yet conventional identification methods often struggle to capture latent dynamics while maintaining stability. We propose a \\textit{stable-by-design LPV neural network-based state-space} (NN-SS) model that simultaneously learns latent states and internal scheduling variables directly from data. The state-transition matrix, generated by a neural network using the learned scheduling variables, is guaranteed to be stable through a Schur-based parameterization. The architecture combines an encoder for initial state estimation with a state-space representer network that constructs the full set of scheduling-dependent system matrices. For training the NN-SS, we develop a framework that integrates multi-step prediction losses with a state-consistency regularization term, ensuring robustness against drift and improving long-horizon prediction accuracy. The proposed NN-SS is evaluated on benchmark nonlinear systems, and the results demonstrate that the model consistently matches or surpasses classical subspace identification methods and recent gradient-based approaches. These findings highlight the potential of stability-constrained neural LPV identification as a scalable and reliable framework for modeling complex nonlinear systems.","short_abstract":"Accurate modeling of nonlinear systems is essential for reliable control, yet conventional identification methods often struggle to capture latent dynamics while maintaining stability. We propose a \\textit{stable-by-design LPV neural network-based state-space} (NN-SS) model that simultaneously learns latent states and...","url_abs":"https://arxiv.org/abs/2510.24757","url_pdf":"https://arxiv.org/pdf/2510.24757v1","authors":"[\"Ahmet Eren Sertbaş\",\"Tufan Kumbasar\"]","published":"2025-10-21T10:25:54Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}