Robust and Kernelized Data-Enabled Predictive Control for Nonlinear Systems
Abstract
This paper presents a robust and kernelized data-enabled predictive control (RoKDeePC) algorithm to perform model-free optimal control for nonlinear systems using only input and output data. The algorithm combines robust predictive control and a non-parametric representation of nonlinear systems enabled by regularized kernel methods. The latter is based on implicitly learning the nonlinear behavior of the system via the representer theorem. Instead of seeking a model and then performing control design, our method goes directly from data to control. This allows us to robustify the control inputs against the uncertainties in data by considering a min-max optimization problem to calculate the optimal control sequence. We show that by incorporating a proper uncertainty set, this min-max problem can be reformulated as a nonconvex but structured minimization problem. By exploiting its structure, we present a projected gradient descent algorithm to effectively solve this problem. Finally, we test the RoKDeePC on two nonlinear example systems - one academic case study and a grid-forming converter feeding a nonlinear load - and compare it with some existing nonlinear data-driven predictive control methods.
Cite
@article{arxiv.2206.01866,
title = {Robust and Kernelized Data-Enabled Predictive Control for Nonlinear Systems},
author = {Linbin Huang and John Lygeros and Florian Dörfler},
journal= {arXiv preprint arXiv:2206.01866},
year = {2022}
}