Data-driven Self-triggered Control via Trajectory Prediction

Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity. However, majority of existing self-triggered control methods require explicit models. An end-to-end paradigm known as data-driven designs laws directly from data and offers competing alternative to routine identification-then-control strategy. In this context, present paper puts forth schemes unknown linear systems using input-output collected offline. Specifically, model predictive (MPC) scheme proposed, which computes sequence inputs generating predicted trajectory. addition, self-triggering mechanism designed, determines next triggering time solution MPC newly measurements. Finally, both feasibility stability are established proposed controller, validated numerical example.