Feedback linearization through the lens of data
Abstract
Controlling nonlinear systems, especially when data are being used to offset uncertainties in the model, is hard. A natural approach when dealing with the challenges of nonlinear control is to reduce the system to a linear one via change of coordinates and feedback, an approach commonly known as feedback linearization. Here we consider the feedback linearization problem of an unknown system when the solution must be found using experimental data. We propose a new method that learns the change of coordinates and the linearizing controller from a library (a dictionary) of candidate functions with a simple algebraic procedure - the computation of the null space of a data-dependent matrix. Remarkably, we show that the solution is valid over the entire state space of interest and not just on the dataset used to determine the solution.
Cite
@article{arxiv.2308.11229,
title = {Feedback linearization through the lens of data},
author = {C. De Persis and D. Gadginmath and F. Pasqualetti and P. Tesi},
journal= {arXiv preprint arXiv:2308.11229},
year = {2024}
}
Comments
This is the extended version of a paper that appeared in abridged form in the Proceedings of the 62nd IEEE Conference on Decision and Control, 13-15 December 2023, Singapore