Robust Self-Triggered Control Approaches Optimizing Sensors Utilization with Asynchronous Measurements

TL;DR

This paper introduces a self-triggered control method for linear systems with asynchronous sensor updates, optimizing sensor usage and maintaining stability, significantly reducing communication without sacrificing performance.

Contribution

It proposes a novel approach that computes optimal sampling horizons and sensor selection, with both online and offline implementations ensuring stability and reducing sensor communication.

Findings

Achieves 59-74% reduction in sensor utilization compared to periodic sampling.

Guarantees exponential stability for unperturbed systems.

Provides global uniform ultimate boundedness for systems with disturbances.

Abstract

Most control systems run on digital hardware with limited communication resources. This work develops self-triggered control for linear systems where sensors update independently (asynchronous measurements). The controller computes an optimal horizon at each sampling instant, selecting which sensor to read over the next several time steps to maximize inter-sample intervals while maintaining stability. Two implementations address computational complexity. The online version solves an optimization problem at each update for theoretical optimality. The offline version precomputes optimal horizons using conic partitioning, reducing online computation to a lookup. Both guarantee exponential stability for unperturbed systems and global uniform ultimate boundedness for systems with bounded disturbances. Simulations demonstrate 59-74\% reductions in sensor utilization compared to periodic sampling. The framework enables resource-efficient control in networked systems with communication constraints.