Data-Driven Nonlinear Regulation: Gaussian Process Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911000014356480 |
|---|---|
| author | Harry, Telema Guay, Martin Wang, Shimin Braatz, Richard D. |
| author_facet | Harry, Telema Guay, Martin Wang, Shimin Braatz, Richard D. |
| contents | This article addresses the output regulation problem for a class of nonlinear systems using a data-driven approach. An output feedback controller is proposed that integrates a traditional control component with a data-driven learning algorithm based on Gaussian Process (GP) regression to learn the nonlinear internal model. Specifically, a data-driven technique is employed to directly approximate the unknown internal model steady-state map from observed input-output data online. Our method does not rely on model-based observers utilized in previous studies, making it robust and suitable for systems with modelling errors and model uncertainties. Finally, we demonstrate through numerical examples and detailed stability analysis that, under suitable conditions, the closed-loop system remains bounded and converges to a compact set, with the size of this set decreasing as the accuracy of the data-driven model improves over time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09273 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Nonlinear Regulation: Gaussian Process Learning Harry, Telema Guay, Martin Wang, Shimin Braatz, Richard D. Systems and Control Discrete Mathematics Optimization and Control Adaptation and Self-Organizing Systems This article addresses the output regulation problem for a class of nonlinear systems using a data-driven approach. An output feedback controller is proposed that integrates a traditional control component with a data-driven learning algorithm based on Gaussian Process (GP) regression to learn the nonlinear internal model. Specifically, a data-driven technique is employed to directly approximate the unknown internal model steady-state map from observed input-output data online. Our method does not rely on model-based observers utilized in previous studies, making it robust and suitable for systems with modelling errors and model uncertainties. Finally, we demonstrate through numerical examples and detailed stability analysis that, under suitable conditions, the closed-loop system remains bounded and converges to a compact set, with the size of this set decreasing as the accuracy of the data-driven model improves over time. |
| title | Data-Driven Nonlinear Regulation: Gaussian Process Learning |
| topic | Systems and Control Discrete Mathematics Optimization and Control Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2506.09273 |