Adaptive Online Model Update Algorithm for Predictive Control in Networked Systems

TL;DR

This paper presents an adaptive online model update algorithm for predictive control in networked systems, enabling real-time model adjustments that improve control performance while reducing communication bandwidth.

Contribution

It introduces a novel real-time model updating method that integrates with existing control algorithms, specifically tailored for non-linear convex models in networked systems.

Findings

Reduced communication bandwidth due to minimized data exchange

Enhanced control performance demonstrated in IEEE test case

Model adapts effectively after disturbances

Abstract

In this article, we introduce an adaptive online model update algorithm designed for predictive control applications in networked systems, particularly focusing on power distribution systems. Unlike traditional methods that depend on historical data for offline model identification, our approach utilizes real-time data for continuous model updates. This method integrates seamlessly with existing online control and optimization algorithms and provides timely updates in response to real-time changes. This methodology offers significant advantages, including a reduction in the communication network bandwidth requirements by minimizing the data exchanged at each iteration and enabling the model to adapt after disturbances. Furthermore, our algorithm is tailored for non-linear convex models, enhancing its applicability to practical scenarios. The efficacy of the proposed method is validated through a numerical study, demonstrating improved control performance using a synthetic IEEE test case.