# Deep-Learning-Based Identification of LPV Models for Nonlinear Systems

arXiv: https://arxiv.org/abs/2204.04060 · HTML: https://mlsanity.com/paper/2204.04060/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2022.

## Built on by (most cited first, 5 of 5)

- [Efficient identification of linear, parameter-varying, and nonlinear systems with noise models](https://mlsanity.com/paper/2504.11982/) (2025)
- [Learning Stable and Robust Linear Parameter-Varying State-Space Models](https://mlsanity.com/paper/2304.01828/) (2023)
- [Initialization Approach for Nonlinear State-Space Identification via the Subspace Encoder Approach](https://mlsanity.com/paper/2304.02119/) (2023)
- [Identification For Control Based on Neural Networks: Approximately Linearizable Models](https://mlsanity.com/paper/2409.15858/) (2024)
- [Online learning of neural state-space models](https://mlsanity.com/paper/2607.17614/) (2026)

## Abstract (arXiv)

The Linear Parameter-Varying (LPV) framework provides a modeling and control design toolchain to address nonlinear (NL) system behavior via linear surrogate models. Despite major research effort on LPV data-driven modeling, a key shortcoming of the current identification theory is that often the scheduling variable is assumed to be a given measured signal in the data set. In case of identifying an LPV model of a NL system, the selection of the scheduling map, which describes the relation to the measurable scheduling signal, is put on the users' shoulder, with only limited supporting tools available. This choice however greatly affects the usability and complexity of the resulting LPV model. This paper presents a deep-learning-based approach to provide joint estimation of a scheduling map and an LPV state-space model of a NL system from input-output data, and has consistency guarantees under general innovation-type noise conditions. Its efficiency is demonstrated on a realistic identification problem.