Online data-enabled predictive control

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

doi:10.1016/j.automatica.2021.109926

Title: Online data-enabled predictive control

Journal Article · 01 February 2022 · Automatica

DOI: https://doi.org/10.1016/j.automatica.2021.109926 · OSTI ID:1845669

Baros, Stefanos ^[1]; Chang, Chin-Yao ^[1]; Colon-Reyes, Gabriel E. ^[2]; Bernstein, Andrey ^[1]

National Renewable Energy Lab. (NREL), Golden, CO (United States)
Univ. of California, Berkeley, CA (United States)

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.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Electricity (OE), Advanced Grid Modeling Program

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1845669

Report Number(s):: NREL/JA--5D00-76265; MainId:5920; UUID:05e23077-9e5d-ea11-9c31-ac162d87dfe5; MainAdminID:61737

Journal Information:: Automatica, Journal Name: Automatica Vol. 138; ISSN 0005-1098

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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