Self-triggered Model Predictive Control for Continuous-Time Systems: A Multiple Discretizations Approach

TL;DR

This paper introduces a self-triggered model predictive control method for continuous-time systems that reduces communication load by selecting optimal sampling patterns through multiple discretizations, ensuring stability.

Contribution

It presents a novel approach combining multiple discretizations in MPC to minimize communication in continuous-time control systems.

Findings

Reduces communication load in control systems.

Maintains system stability with fewer transmissions.

Demonstrates effectiveness through simulation examples.

Abstract

In this paper, we propose a new self-triggered formulation of Model Predictive Control for continuous-time linear networked control systems. Our control approach, which aims at reducing the number of transmitting control samples to the plant, is derived by parallelly solving optimal control problems with different sampling time intervals. The controller then picks up one sampling pattern as a transmission decision, such that a reduction of communication load and the stability will be obtained. The proposed strategy is illustrated through comparative simulation examples.