Neural Event-Triggered Control with Optimal Scheduling

TL;DR

This paper presents Neural ETC, a novel framework for event-triggered control with optimal scheduling, significantly reducing communication resources while maintaining stability and performance in resource-constrained control systems.

Contribution

The paper introduces Neural ETC with two algorithms for optimal event-triggered control and provides theoretical guarantees for stability and schedule optimality.

Findings

Neural ETC reduces communication resources compared to traditional neural controllers.

The algorithms achieve minimal triggering times while ensuring system stability.

Empirical results demonstrate improved control performance under communication constraints.

Abstract

Learning-enabled controllers with stability certificate functions have demonstrated impressive empirical performance in addressing control problems in recent years. Nevertheless, directly deploying the neural controllers onto actual digital platforms requires impractically excessive communication resources due to a continuously updating demand from the closed-loop feedback controller. We introduce a framework aimed at learning the event-triggered controller (ETC) with optimal scheduling, i.e., minimal triggering times, to address this challenge in resource-constrained scenarios. Our proposed framework, denoted by Neural ETC, includes two practical algorithms: the path integral algorithm based on directly simulating the event-triggered dynamics, and the Monte Carlo algorithm derived from new theoretical results regarding lower bound of inter-event time. Furthermore, we propose a projection operation with an analytical expression that ensures theoretical stability and schedule optimality for Neural ETC. Compared to the conventional neural controllers, our empirical results show that the Neural ETC significantly reduces the required communication resources while enhancing the control performance in constrained communication resources scenarios.