One of the main paradigms in the field of systems and control is that of model-based control. In practice, model-based control techniques involve a two-step control procedure consisting of system identification followed by control design. In contrast, data-driven control design aims to construct controllers and implementation strategies directly from data without (explicitly) identifying the system model. Traditional approaches require control to be updated periodically, leading to overuse of resources and communication bandwidth. The essential idea in an event based control is to update the control input only when necessary.

In this seminar, we present data-driven event based control strategies for discrete-time linear systems. The results presented do not require explicit identification of the system parameters and are based on the input and state data collected from the system during an open-loop experiment. The design of event-triggered control consists of two stages: finding a state feedback controller that exponentially stabilizes the system and designing an event-triggered policy that determines the instances at which the control law needs to be updated to guarantee pre-specified exponential convergence. It is also ensured that the events are not triggered at all discrete-time instances (non-trivial event-triggering). We present simulation results for an illustrative example that validates the proposed methods.