Robust Self-Triggered Control Approaches Optimizing Sampling Sequences with Synchronous Measurements

TL;DR

This paper presents a novel self-triggered control method for linear systems that optimizes sampling sequences to improve resource efficiency while guaranteeing stability and robustness against disturbances.

Contribution

It introduces an optimal self-triggering scheme that precomputes sampling sequences, ensuring exponential stability and robustness in sampled-data control systems.

Findings

Guarantees exponential stability for unperturbed systems.

Ensures global uniform ultimate boundedness for perturbed systems.

Demonstrates improved resource efficiency through simulations.

Abstract

Feedback control algorithms traditionally rely on periodic execution on digital platforms. While this simplifies design and analysis, it often leads to inefficient resource usage (e.g., CPU, network bandwidth) in embedded control and shared networks. This work investigates self-triggering implementations of linear controllers in sampled-data systems with synchronous measurements. Our approach precomputes the next sampling sequence over a finite horizon based on current state information. We introduce a novel optimal self-triggering scheme that guarantees exponential stability for unperturbed systems and global uniform ultimate boundedness for perturbed systems. This ensures robustness against external disturbances with explicit performance guarantees. Simulations demonstrate the benefits of our approach.