ScholarWorks@경상국립대학교

초록

The use of data-driven techniques has emerged as a novel approach in modern control engineering, bypassing the need for explicit system identification. This paper introduces a robust data-driven control methodology for stabilizing unknown systems with uncertainties. The approach offers two key contributions: first, Theorem 1 introduces an optimization framework that leverages linear matrix inequality (LMI) formulations to streamline the design of robust controllers. Second, Theorem 2 provides a sufficient criterion to ensure the asymptotic stability of the closed-loop system while enhancing its performance in the presence of uncertain matrices. Simulation studies validate the method's effectiveness, demonstrating its capability to achieve robust stabilization and enhance system performance.