Online Data-Enabled Predictive Control

Research output: NRELPoster


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.
Original languageAmerican English
StatePublished - 2020

Publication series

NamePresented at the Forging Connections Between Machine Learning, Data Science, and Power Systems Research Workshop, 5-6 March 2020, Alexandria, Virginia

NREL Publication Number

  • NREL/PO-5D00-76263


  • data-driven
  • optimization


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