Online Data-Enabled Predictive Control

Stefanos Baros, Chin-Yao Chang, Gabriel Colon-Reyes, Andrey Bernstein

Research output: Contribution to journalArticlepeer-review

17 Scopus Citations

Abstract

We develop an online data-enabled predictive (ODeePC) control method for optimal control of unknown systems, building on the recently proposed DeePC (Coulson et al., 2019). 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. The proposed ODeePC conceptual-wise resembles standard adaptive system identification and model predictive control (MPC), but it provides a new alternative for the standard methods. ODeePC is enabled by computationally efficient methods that exploit the special structure of the Hankel matrices in the context of DeePC with Fast Fourier Transform (FFT) and primal–dual algorithm We provide theoretical guarantees regarding the asymptotic behavior of ODeePC, and we demonstrate its performance through numerical examples.

Original languageAmerican English
Article number109926
Number of pages9
JournalAutomatica
Volume138
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2021

NREL Publication Number

  • NREL/JA-5D00-76265

Keywords

  • Data-driven control
  • Model predictive control
  • Online optimization

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