@misc{02a97d7fe2b8495199b60137177e0698,
title = "Online Data-Enabled Predictive Control",
abstract = "We develop an online data-enabled predictive (ODeePC) control method for trajectory tracking of unknown systems, building upon the recently proposed DeePC. Our proposed ODeePC method leverages a primal-dual algorithm with real-time measurement feedback to iteratively compute the corresponding real-time optimal control policy as system conditions change. Specifically, our developed ODeePC: a) records data from the unknown system and updates the underlying primal-dual algorithm dynamically, b) can track changes in the system's operating point and adjust the control inputs, and c) is computationally efficient as it deploys a Fast Fourier Transform-based algorithm enabling the fast computation of the product of a non-square Hankel matrix with a vector. We provide theoretical guarantees regarding the asymptotic behavior of ODeePC and demonstrate its performance through a power system application.",
keywords = "data-driven, optimization",
author = "Chin-Yao Chang and Stefanos Baros and Andrey Bernstein",
year = "2020",
language = "American English",
series = "Presented at the Forging Connections Between Machine Learning, Data Science, and Power Systems Research Workshop, 5-6 March 2020, Alexandria, Virginia",
type = "Other",
}