Event-triggered Control From Data

TL;DR

This paper introduces a data-driven method for designing event-triggered controllers for unknown linear systems, ensuring stability and avoiding Zeno behavior even with disturbances, using offline collected noisy data.

Contribution

It develops a novel data-based approach to design robust event-triggered controllers that guarantee stability and positive inter-event times for networked systems.

Findings

Ensures input-to-state stability with data-driven controllers.

Provides triggering strategies that prevent Zeno phenomena.

Applicable to various existing triggering rules.

Abstract

We present a data-based approach to design event-triggered state-feedback controllers for unknown continuous-time linear systems affected by disturbances. By an event, we mean state measurements transmission from the sensors to the controller over a digital network. By exploiting a sufficiently rich finite set of noisy state measurements and inputs collected off-line, we first design a data-driven state-feedback controller to ensure an input-to-state stability property for the closed-loop system ignoring the network. We then take into account sampling induced by the network and we present robust data-driven triggering strategies to (approximately) preserve this stability property. The approach is general in the sense that it allows deriving data-based versions of various popular triggering rules of the literature. In all cases, the designed transmission policies ensure the existence of a (global) strictly positive minimum inter-event times thereby excluding Zeno phenomenon despite disturbances. These results can be viewed as a step towards plug-and-play control for networked control systems, i.e., mechanisms that automatically learn to control and to communicate over a network.