Efficient Learning of Affine and Rational Dependency LPV Models With Linear Fractional Representation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913118565695488 |
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| author | Drenth, Roel Hoekstra, Jan H. Schoukens, Maarten Tóth, Roland |
| author_facet | Drenth, Roel Hoekstra, Jan H. Schoukens, Maarten Tóth, Roland |
| contents | Identifying control-friendly models of nonlinear systems remains one of the major challenges at the intersection of system identification and control. The Linear Parameter-Varying (LPV) framework offers a promising solution, but existing identification methods often rely on model structures with affine scheduling dependency. Instead, this work proposes the use of LPV models with Linear Fractional Representation (LFR) admitting a rational scheduling-dependency, capable of modelling complex nonlinear systems with fewer scheduling variables compared to affine models. This work introduces a direct parameterization to ensure well-posedness of rational LPV-LFR models, which by joint-estimation of an LPV plant and scheduling map, using only input-output data, is capable of modelling complex nonlinear systems. Accuracy of the proposed approach is shown on two simulation examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12203 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient Learning of Affine and Rational Dependency LPV Models With Linear Fractional Representation Drenth, Roel Hoekstra, Jan H. Schoukens, Maarten Tóth, Roland Systems and Control Identifying control-friendly models of nonlinear systems remains one of the major challenges at the intersection of system identification and control. The Linear Parameter-Varying (LPV) framework offers a promising solution, but existing identification methods often rely on model structures with affine scheduling dependency. Instead, this work proposes the use of LPV models with Linear Fractional Representation (LFR) admitting a rational scheduling-dependency, capable of modelling complex nonlinear systems with fewer scheduling variables compared to affine models. This work introduces a direct parameterization to ensure well-posedness of rational LPV-LFR models, which by joint-estimation of an LPV plant and scheduling map, using only input-output data, is capable of modelling complex nonlinear systems. Accuracy of the proposed approach is shown on two simulation examples. |
| title | Efficient Learning of Affine and Rational Dependency LPV Models With Linear Fractional Representation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2605.12203 |