# LPVcore: MATLAB Toolbox for LPV Modelling, Identification and Control

arXiv: https://arxiv.org/abs/2105.03695 · HTML: https://mlsanity.com/paper/2105.03695/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2021.

## Built on by (most cited first, 3 of 3)

- [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)
- [A finite-sample generalization bound for stable LPV systems](https://mlsanity.com/paper/2405.10054/) (2024)

## Abstract (arXiv)

This paper describes the LPVcore software package for MATLAB developed to model, simulate, estimate and control systems via linear parameter-varying (LPV) input-output (IO), state-space (SS) and linear fractional (LFR) representations. In the LPVcore toolbox, basis affine parameter-varying matrix functions are implemented to enable users to represent LPV systems in a global setting, i.e., for time-varying scheduling trajectories. This is a key difference compared to other software suites that use a grid or only LFR-based representations. The paper contains an overview of functions in the toolbox to simulate and identify IO, SS and LFR representations. Based on various prediction-error minimization methods, a comprehensive example is given on the identification of a DC motor with an unbalanced disc, demonstrating the capabilities of the toolbox. The software and examples are available on www.lpvcore.net.