Event-triggered Pulse Control with Model Learning (if Necessary)
For networked control systems where communication is scarce and accurate models are unavailable, this method offers a practical solution by combining event-triggered control with model learning.
This paper proposes an event-triggered pulse control strategy that learns dynamics models when needed, achieving high performance with reduced communication samples while adapting to changing dynamics and replacing the integral part in periodic control.
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.