Optimal Delay Compensation in Networked Predictive Control

TL;DR

This paper introduces a systematic approach to selecting the optimal delay bound in Networked Predictive Control, balancing prediction errors and robustness to communication losses for improved system performance.

Contribution

It develops a method to determine the optimal delay bound, enhancing the robustness and efficiency of Networked Predictive Control systems.

Findings

Simulation results show significant performance improvements with the optimal delay bound.

The method effectively balances prediction accuracy and robustness to delays and dropouts.

Abstract

Networked Predictive Control is widely used to mitigate the effect of delays and dropouts in Networked Control Systems, particularly when these exceed the sampling time. A key design choice of these methods is the delay bound, which determines the prediction horizon and the robustness to information loss. This work develops a systematic method to select the optimal bound by quantifying the trade-off between prediction errors and open-loop operation caused by communication losses. Simulation studies demonstrate the performance gains achieved with the optimal bound.