Uncertainty-based perturb and observe for data-driven optimization
Abstract
Data-based adaptive optimization methods hold great promise for the performance optimization of uncertain, time-varying processes. However, current methods are often based on continuous perturbation which is in general undesired for real-life (e.g., industrial) applications. In this paper, a new uncertainty-based perturb-and-observe method is developed that addresses this limitation and reduces the required number of perturbations, while retaining the capability to track time-varying optima. The method is based on the philosophy of `only perturbing when needed,' and is shown to converge to the optimum under mild conditions. A simulation-based case study on a photo-voltaic solar array demonstrates that it can outperform the standard perturb and observe approach as well as three other data-based optimization methods.
Cite
@article{arxiv.2604.15922,
title = {Uncertainty-based perturb and observe for data-driven optimization},
author = {Leontine Aarnoudse and Mark Haring and Nathan van de Wouw and Alexey Pavlov},
journal= {arXiv preprint arXiv:2604.15922},
year = {2026}
}
Comments
16 pages, 7 figures. This work has been submitted to the IEEE for possible publication