MDR-DeePC: Model-Inspired Distributionally Robust Data-Enabled Predictive Control
Abstract
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.
Keywords
Cite
@article{arxiv.2506.19744,
title = {MDR-DeePC: Model-Inspired Distributionally Robust Data-Enabled Predictive Control},
author = {Shihao Li and Jiachen Li and Christopher Martin and Soovadeep Bakshi and Dongmei Chen},
journal= {arXiv preprint arXiv:2506.19744},
year = {2025}
}
Comments
Submitted to MECC 2025