Data-Driven Event-Triggered Control for Discrete-Time LTI Systems with Exogenous Inputs

Many industrial processes, computing devices, and networks, such as traffic systems and power grids, exhibit complex dynamics. The model capturing the true dynamics may not be readily available for these systems. Due to advanced sensing technologies, it is possible to collect large amounts of real-time data. Leveraging this data offers an opportunity to design data-driven control without constructing an explicit model for the system. In this paper, we derive data-dependent matrices from an ensemble of input-state trajectories to parameterize the closed-loop Linear Time-Invariant (LTI) system, accounting for exogenous inputs. The stabilizing control law is designed in such a way that it gets updated based on events, where an event refers to the violation of certain performance conditions. The results proposed are implemented on a computing system, in particular demonstrating auto-scaling of web servers hosted on a private cloud.