Data-Driven Dynamic Event-Triggered Control

This article revisits the event-triggered control problem from a data-driven perspective, where unknown continuous-time linear systems subject to disturbances are taken into account. Using data information collected offline instead of accurate system model information, a data-driven dynamic event-triggered control scheme is developed in this article. An update algorithm is proposed for dynamically updating the event-triggered function embedded in the event-triggered mechanism (ETM). Thanks to the designed dynamic ETM, a strictly positive minimum inter-event time is guaranteed without sacrificing control performance. Specifically, exponential input-to-state stability (ISS) of the closed-loop system with respect to disturbances is achieved in this article, which is superior to some existing results that only guarantee a practical exponential ISS property. The dynamic ETM is easy-to-implement in practical operation since all designed parameters are determined only by a simple data-driven linear matrix inequality, without additional complicated conditions as required in relevant literature. As quantization is the most common signal constraint in practice, the developed control scheme is further extended to the case where state transmission is affected by a uniform or logarithmic quantization effect. Finally, adequate simulations are performed to show the validity of the proposed control schemes.