Robust Data-Enabled Predictive Control: Tractable Formulations and Performance Guarantees
Abstract
We introduce a general framework for robust data-enabled predictive control (DeePC) for linear time-invariant (LTI) systems. The proposed framework enables us to obtain model-free optimal control for LTI systems based on noisy input/output data. More specifically, robust DeePC solves a min-max optimization problem to compute the optimal control sequence that is resilient to all possible realizations of the uncertainties in the input/output data within a prescribed uncertainty set. We present computationally tractable reformulations of the min-max problem with various uncertainty sets. Furthermore, we show that even though an accurate prediction of the future behavior is unattainable in practice due to inaccessibility of the perfect input/output data, the obtained robust optimal control sequence provides performance guarantees for the actually realized input/output cost. We further show that the robust DeePC generalizes and robustifies the regularized DeePC (with quadratic regularization or 1-norm regularization) proposed in the literature. Finally, we demonstrate the performance of the proposed robust DeePC algorithm on high-fidelity, nonlinear, and noisy simulations of a grid-connected power converter system.
Cite
@article{arxiv.2105.07199,
title = {Robust Data-Enabled Predictive Control: Tractable Formulations and Performance Guarantees},
author = {Linbin Huang and Jianzhe Zhen and John Lygeros and Florian Dörfler},
journal= {arXiv preprint arXiv:2105.07199},
year = {2021}
}