Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.02797 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911471256993792 |
|---|---|
| author | Zieglmeier, Sebastian de Badyn, Mathias Hudoba Warakagoda, Narada D. Krogstad, Thomas R. Engelstad, Paal |
| author_facet | Zieglmeier, Sebastian de Badyn, Mathias Hudoba Warakagoda, Narada D. Krogstad, Thomas R. Engelstad, Paal |
| contents | This paper presents a Gain-Scheduled Data-Enabled Predictive Control (GS-DeePC) framework for nonlinear systems based on multiple locally linear data representations. Instead of relying on a single global Hankel matrix, the operating range of a measurable scheduling variable is partitioned into regions, and regional Hankel matrices are constructed from persistently exciting data. To ensure smooth transitions between linearization regions and suppress region-induced chattering, composite regions are introduced, merging neighboring data sets and enabling a robust switching mechanism. The proposed method maintains the original DeePC problem structure and can achieve reduced computational complexity by requiring only short, locally informative data sequences. Extensive experiments on a nonlinear DC-motor with an unbalanced disc demonstrate the significantly improved control performance compared to standard DeePC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_02797 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Gain-Scheduling Data-Enabled Predictive Control for Nonlinear Systems with Linearized Operating Regions Zieglmeier, Sebastian de Badyn, Mathias Hudoba Warakagoda, Narada D. Krogstad, Thomas R. Engelstad, Paal Systems and Control This paper presents a Gain-Scheduled Data-Enabled Predictive Control (GS-DeePC) framework for nonlinear systems based on multiple locally linear data representations. Instead of relying on a single global Hankel matrix, the operating range of a measurable scheduling variable is partitioned into regions, and regional Hankel matrices are constructed from persistently exciting data. To ensure smooth transitions between linearization regions and suppress region-induced chattering, composite regions are introduced, merging neighboring data sets and enabling a robust switching mechanism. The proposed method maintains the original DeePC problem structure and can achieve reduced computational complexity by requiring only short, locally informative data sequences. Extensive experiments on a nonlinear DC-motor with an unbalanced disc demonstrate the significantly improved control performance compared to standard DeePC. |
| title | Gain-Scheduling Data-Enabled Predictive Control for Nonlinear Systems with Linearized Operating Regions |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.02797 |