Event-triggered control of nonlinear systems from data

TL;DR

This paper extends data-driven event-triggered control methods from linear to nonlinear systems, providing Lyapunov-based designs with guaranteed inter-event times and data-tuned parameters, demonstrated through numerical examples.

Contribution

It introduces two novel data-based event-triggered control policies for nonlinear systems, utilizing Lyapunov functions for certification and parameter tuning from data.

Findings

Guarantees positive minimum inter-event time

Designs rely on Lyapunov functions certified from data

Numerical illustrations demonstrate effectiveness

Abstract

In a recent paper [8], we introduced a data-based approach to design event-triggered controllers for linear systems directly from data. Here, we extend the results in [8] to a class of nonlinear systems. We provide two data-based designs certified by a (classical) Lyapunov function. For these two designs, we devise event-triggered policies that rely on the previously found Lyapunov function, have parameters tuned from data, ensure a positive minimum inter-event time, and act based either on the state error or on the library error. These two different policies, and their respective advantages, are illustrated numerically.