Online Data-Enabled Predictive Control

Conference · Wed Sep 30 00:00:00 EDT 2020

OSTI ID:1677434

Chang, Chin-Yao; Baros, Stefanos; Bernstein, Andrey

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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Electricity Delivery and Energy Reliability (OE)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1677434
• Report Number(s): NREL/PO-5D00-76263; MainId:6341; UUID:65456711-9b5d-ea11-9c31-ac162d87dfe5; MainAdminID:18603
• Conference Information: Presented at the Forging Connections Between Machine Learning, Data Science, and Power Systems Research Workshop, 5-6 March 2020, Alexandria, Virginia
• Country of Publication: United States
• Language: English

Related Subjects

61 RADIATION PROTECTION AND DOSIMETRY
data-driven
optimization