Data‐Driven Dynamic Periodic Event‐Triggered Control for Uncertain Linear Systems With Guaranteed Extended Dissipativity

ABSTRACT This paper investigates the problem of data‐driven dynamic periodic event‐triggered control (DPETC) for uncertain (unknown) linear time‐invariant (LTI) systems by integrating prior knowledge and sampled data. In this DPETC scheme, system states are only sampled at discrete times, and an event‐triggered mechanism (ETM) is designed to determine whether to transmit the sampled data to the controller side. Firstly, we reformulate the closed‐loop system as a time‐delay system and derive sufficient conditions for extended dissipativity using the Lyapunov‐Krasovskii functional method. Then, we obtain data‐dependent co‐design conditions for feedback gain and event‐triggering parameters. Our approach eliminates the need for dual tricks commonly used in data‐driven control and improves feasibility by involving more free scalars. Finally, two numerical examples and an aero engine hardware‐in‐the‐loop (HIL) simulation experiment demonstrate the effectiveness of our approach.