On the robustness of self-triggered sampling of nonlinear control systems

TL;DR

This paper introduces a robust self-triggered sampling method for nonlinear control systems that improves resilience to model uncertainties and bridges the gap between event-triggered and self-triggered paradigms.

Contribution

It proposes a novel robust self-triggered sampling approach that generalizes existing methods and enhances robustness against model uncertainties in nonlinear control systems.

Findings

The new method reduces sampling conservativeness.

It improves closed-loop system performance.

It outperforms existing self-triggered samplers in robustness.

Abstract

We address robustness issues of self-triggered sampling with respect to model uncertainties, and propose a robust self-triggered sampling method. The approach is compared with existing methods in terms of sampling conservativeness and closed-loop system performance. The proposed method aims at fulfilling the gap between the event and the self-triggered sampling paradigms for what concerns robustness with respect to model uncertainties, and it generalizes most of the existing self-triggered samplers implemented up to now.