MDR-DeePC: Model-Inspired Distributionally Robust Data-Enabled Predictive Control

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Detalles Bibliográficos
Autores principales:	Li, Shihao, Li, Jiachen, Martin, Christopher, Bakshi, Soovadeep, Chen, Dongmei
Formato:	Preprint
Publicado:	2025
Materias:	Systems and Control
Acceso en línea:	Acceder al recurso
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author	Li, Shihao Li, Jiachen Martin, Christopher Bakshi, Soovadeep Chen, Dongmei
author_facet	Li, Shihao Li, Jiachen Martin, Christopher Bakshi, Soovadeep Chen, Dongmei
contents	This paper presents a Model-Inspired Distributionally Robust Data-enabled Predictive Control (MDR-DeePC) framework for systems with partially known and uncertain dynamics. The proposed method integrates model-based equality constraints for known dynamics with a Hankel matrix-based representation of unknown dynamics. A distributionally robust optimization problem is formulated to account for parametric uncertainty and stochastic disturbances. Simulation results on a triple-mass-spring-damper system demonstrate improved disturbance rejection, reduced output oscillations, and lower control cost compared to standard DeePC. The results validate the robustness and effectiveness of MDR-DeePC, with potential for real-time implementation pending further benchmarking.
format	Preprint
id	arxiv_https___arxiv_org_abs_2506_19744
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	MDR-DeePC: Model-Inspired Distributionally Robust Data-Enabled Predictive Control Li, Shihao Li, Jiachen Martin, Christopher Bakshi, Soovadeep Chen, Dongmei Systems and Control This paper presents a Model-Inspired Distributionally Robust Data-enabled Predictive Control (MDR-DeePC) framework for systems with partially known and uncertain dynamics. The proposed method integrates model-based equality constraints for known dynamics with a Hankel matrix-based representation of unknown dynamics. A distributionally robust optimization problem is formulated to account for parametric uncertainty and stochastic disturbances. Simulation results on a triple-mass-spring-damper system demonstrate improved disturbance rejection, reduced output oscillations, and lower control cost compared to standard DeePC. The results validate the robustness and effectiveness of MDR-DeePC, with potential for real-time implementation pending further benchmarking.
title	MDR-DeePC: Model-Inspired Distributionally Robust Data-Enabled Predictive Control
topic	Systems and Control
url	https://arxiv.org/abs/2506.19744

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