Noise-Tolerant Hybrid Approach for Data-Driven Predictive Control

2025/06/25 by Mazare, Mahmood, Ramezani, Hossein · 1 citation
#FOS: Electrical engineering #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2506.20780

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.

Citations

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Cited by

• Robust Offset-free Kernelized Data-Driven Predictive Control for Nonlinear Systems

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