# Event-triggered Pulse Control with Model Learning (if Necessary)

## TL;DR

This paper introduces an event-triggered pulse control strategy that adaptively learns system dynamics when needed, reducing communication in networked control systems while maintaining high performance.

## Contribution

It presents a novel control approach that combines event-triggered control with model learning, enabling effective control without prior accurate models.

## Key findings

- Reduces communication in control systems.
- Adapts to changing system dynamics.
- Eliminates need for pre-existing accurate models.

## Abstract

In networked control systems, communication is a shared and therefore scarce resource. Event-triggered control (ETC) can achieve high performance control with a significantly reduced amount of samples compared to classical, periodic control schemes. However, ETC methods usually rely on the availability of an accurate dynamics model, which is oftentimes not readily available. In this paper, we propose a novel event-triggered pulse control strategy that learns dynamics models if necessary. In addition to adapting to changing dynamics, the method also represents a suitable replacement for the integral part typically used in periodic control.