Online Data-Enabled Predictive Control

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

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DOE OSTI · 1677434

Online Data-Enabled Predictive Control

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

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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.

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BibTeX RIS

Chang, Chin-Yao, Baros, Stefanos, Bernstein, Andrey. 2020-09-30. Online Data-Enabled Predictive Control. https://www.osti.gov/biblio/1677434

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