Noise-Tolerant Hybrid Approach for Data-Driven Predictive Control

TL;DR

This paper introduces a noise-tolerant hybrid data-driven predictive control framework that uses singular value decomposition to improve robustness and efficiency in the presence of measurement noise.

Contribution

It presents a novel NTDPC framework that effectively separates system dynamics from noise, enabling accurate predictions with shorter data horizons.

Findings

Enhanced robustness against measurement noise

Reduced computational effort in trajectory prediction

Improved prediction accuracy over existing methods

Abstract

This paper focuses on a key challenge in hybrid data-driven predictive control: the effect of measurement noise on Hankel matrices. While noise is handled in direct and indirect methods, hybrid approaches often overlook its impact during trajectory estimation. We propose a Noise-Tolerant Data-Driven Predictive Control (NTDPC) framework that integrates singular value decomposition to separate system dynamics from noise within reduced-order Hankel matrices. This enables accurate prediction with shorter data horizons and lower computational effort. A sensitivity index is introduced to support horizon selection under different noise levels. Simulation results indicate improved robustness and efficiency compared to existing hybrid methods.