A Resource-Aware Approach to Self-Triggered Model Predictive Control: Extended Version

TL;DR

This paper introduces a resource-aware self-triggered model predictive control scheme that dynamically manages resource constraints like communication and energy, ensuring feasibility and convergence while optimizing resource usage.

Contribution

It proposes a novel self-triggered MPC framework incorporating pointwise-in-time resource constraints, guaranteeing resource usage bounds and stability.

Findings

Guarantees transient and asymptotic resource constraints.

Ensures recursive feasibility and convergence.

Validated through a numerical example.

Abstract

In this paper, we consider a self-triggered formulation of model predictive control. In this variant, the controller decides at the current sampling instant itself when the next sample should be taken and the optimization problem be solved anew. We incorporate a pointwise-in-time resource constraint into the optimization problem, whose exact form can be chosen by the user. Thereby, the proposed scheme is made resource-aware with respect to a universal resource, which may pertain in practice for instance to communication, computation, energy or financial resources. We show that by virtue of the pointwise-in-time constraints, also a transient and an asymptotic average constraint on the resource usage are guaranteed. Furthermore, we derive conditions on the resource under which the proposed scheme achieves recursive feasibility and convergence. Finally, we demonstrate our theoretical results in a numerical example.