Robust Data-Driven Receding Horizon Control
Jian Zheng, Sahand Kiani, Mario Sznaier, Constantino Lagoa
TL;DR
This work develops a robust data-driven receding horizon control (DDRHC) framework for unknown discrete-time LTI systems subject to bounded disturbances and constraints. By replacing Willems' fundamental lemma with set-membership constraints and online refinement of the consistency set, the method guarantees Uniformly Ultimately Bounded performance for all systems compatible with the observed data. A key contribution is a reduced-complexity dual LP formulation based on Extended Farkas' Lemma that avoids vertex enumeration and enables online adaptation, together with a procedure to compute the largest robust controlled invariant set under state constraints. Numerical results show DDRHC achieving faster contraction and improved contractivity compared with regular data-driven control, validating the approach for constrained, uncertain systems and highlighting its potential for real-time robust control.
Abstract
This paper presents a data-driven receding horizon control framework for discrete-time linear systems that guarantees robust performance in the presence of bounded disturbances. Unlike the majority of existing data-driven predictive control methods, which rely on Willem's fundamental lemma, the proposed method enforces set-membership constraints for data-driven control and utilizes execution data to iteratively refine a set of compatible systems online. Numerical results demonstrate that the proposed receding horizon framework achieves better contractivity for the unknown system compared with regular data-driven control approaches.