Data‐Driven Stabilization of Linear Discrete‐Time Delay Systems With Noisy Data

ABSTRACT This paper is concerned with the stabilization of linear discrete‐time delay systems with unknown system matrices. The objective is to design stabilizing controllers using input and state measurements collected from experiments, which are affected by process disturbances. Assuming that these unknown disturbances are upper‐bounded, the pair of system matrices is represented as a data‐based nominal matrix plus a norm‐bounded uncertain matrix. By utilizing the Lyapunov functional approach, sufficient criteria are derived to design control gains that ensure asymptotic stability of the closed‐loop system. Simulation results validate the effectiveness of the proposed approach, demonstrating an extended allowable delay range compared to some recent methods.