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

TL;DR

MDR-DeePC is a novel control framework that combines model-based constraints with data-driven methods, employing distributionally robust optimization to handle uncertainties, leading to improved disturbance rejection and control performance.

Contribution

It introduces a distributionally robust data-enabled predictive control method that integrates known and unknown system dynamics for enhanced robustness.

Findings

Improved disturbance rejection in simulations.

Reduced output oscillations compared to standard DeePC.

Lower control cost demonstrated in the triple-mass-spring-damper system.

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.