Data-Driven Event-Triggered Scheme for Model-Unknown Fractional-Order Networked Control Systems: A Parametrization Transform Method

This paper proposes a parametrization transform method for model-unknown networked control systems by using a data-driven event-triggered scheme. The key contribution is that an easy-to-apply parametrization transform method is proposed to convert the model-based linear matrix inequality (LMI) conditions into data-driven ones. Compared with existing ones, using the proposed transform method is without requirements on the specified sizes, structures, and unknown system matrices’ positions of model-based LMI conditions. On this basis, by using Lyapunov theory and some inequality techniques, some data-driven condition are derived to guarantee stability. Without considering model dynamics, the controller gain and trigger parameters can be easily derived by learning from collecting offline data packets. Finally, an illustrative example is presented to showcase the outcomes.