Robust Resource-Aware Self-triggered Model Predictive Control

TL;DR

This paper introduces a robust self-triggered model predictive control method that optimizes control performance while efficiently managing limited resources in wireless IoT devices, ensuring reliable operation under uncertainty.

Contribution

It proposes a novel zero-order-hold aperiodic discrete-time feedback law for robust constraint satisfaction in resource-constrained environments.

Findings

Ensures robust constraint satisfaction in uncertain environments.

Optimizes resource usage in wireless IoT control systems.

Provides a new control law for resource-aware operation.

Abstract

The wide adoption of wireless devices in the Internet of Things requires controllers that are able to operate with limited resources, such as battery life. Operating these devices robustly in an uncertain environment, while managing available resources, increases the difficultly of controller design. This paper proposes a robust self-triggered model predictive control approach to optimize a control objective while managing resource consumption. In particular, a novel zero-order-hold aperiodic discrete-time feedback control law is developed to ensure robust constraint satisfaction for continuous-time linear systems.