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

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.