Optimization control of time-varying cyber–physical systems via dynamic-triggered strategies

A novel approach is proposed for designing control strategies for time-varying cyber–physical systems (CPSs) with unknown dynamics, eliminating the need for system identification. Combining with the dynamic-triggered strategies (DTSs), the closed-loop system is parameterized using matrices that are depended on data obtained from a collection of input-state trajectories gathered offline. Additionally, the problem of data-driven optimization control is elegantly resolved through the utilization of classical linear quadratic regulator (LQR) technology, showcasing a remarkable innovation by obviating the necessity for the specific mathematical model of CPSs proposed in this paper. A numerical illustration is provided to illustrate these findings.