Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.20780 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911361099890688 |
|---|---|
| author | Mazare, Mahmood Ramezani, Hossein |
| author_facet | Mazare, Mahmood Ramezani, Hossein |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20780 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Noise-Tolerant Hybrid Approach for Data-Driven Predictive Control Mazare, Mahmood Ramezani, Hossein Systems and Control 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. |
| title | Noise-Tolerant Hybrid Approach for Data-Driven Predictive Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2506.20780 |