2023/09/29 by Yifan Xie, Julian Berberich, Xie, Yifan +3
Engineering · #Advanced Control Systems Optimization #Control Systems and Identification #FOS: Electrical engineering #Fault Detection and Control Systems #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2309.17307
openalex publication_date 2023/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Designing data-driven controllers in the presence of noise is an important research problem, in particular when guarantees on stability, robustness, and constraint satisfaction are desired. In this paper, we propose a data-driven min-max model predictive control (MPC) scheme to design state-feedback controllers from noisy data for unknown linear time-invariant (LTI) system. The considered min-max problem minimizes the worst-case cost over the set of system matrices consistent with the data. We show that the resulting optimization problem can be reformulated as a semidefinite program (SDP). By solving the SDP, we obtain a state-feedback control law that stabilizes the closed-loop system and guarantees input and state constraint satisfaction. A numerical example demonstrates the validity of our theoretical results.