Control Structure-Agnostic Event-Triggering

Traditional event-triggering architectures rely on the knowledge of the structure of continuous- or discrete-time control laws. However, not every control law necessarily possesses a known structure in a closed form. This is especially the case when they are generated by humans, machine learning or artificial intelligence algorithms, or computational optimization or model predictive control methods. Motivated by this observation, the contribution of this paper is to make the first attempt at addressing this scientific gap, where we propose a novel control structure-agnostic event-triggering framework that relies solely on open-loop system dynamics. In addition to the presented system-theoretical analyses of the proposed framework, two illustrative numerical examples respectively involving a human subject and a reinforcement learning strategy are provided to demonstrate the efficacy of our contribution.