Resource-Aware Stochastic Self-Triggered Model Predictive Control

TL;DR

This paper introduces a resource-aware stochastic model predictive control method that balances system performance and resource consumption in uncertain environments with adversarial disturbances.

Contribution

It proposes a novel stochastic predictive control scheme with a zero-order hold feedback to handle resource constraints and uncertainties in system operation.

Findings

Effective scheduling of control updates under resource limitations.

Robustness against adversarial disturbances demonstrated.

Improved resource efficiency compared to traditional methods.

Abstract

This paper considers the control of uncertain systems that are operated under limited resource factors, such as battery life or hardware longevity. We consider here resource-aware self-triggered control techniques that schedule system operation non-uniformly in time in order to balance performance against resource consumption. When running in an uncertain environment, unknown disturbances may deteriorate system performance by acting adversarially against the planned event triggering schedule. In this work, we propose a resource-aware stochastic predictive control scheme to tackle this challenge, where a novel zero-order hold feedback control scheme is proposed to accommodate a time-inhomogeneous predictive control update.