Towards a unifying framework for data-driven predictive control with quadratic regularization
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910397335863296 |
|---|---|
| author | Klädtke, Manuel Darup, Moritz Schulze |
| author_facet | Klädtke, Manuel Darup, Moritz Schulze |
| contents | Data-driven predictive control (DPC) has recently gained popularity as an alternative to model predictive control (MPC). Amidst the surge in proposed DPC frameworks, upon closer inspection, many of these frameworks are more closely related (or perhaps even equivalent) to each other than it may first appear. We argue for a more formal characterization of these relationships so that results can be freely transferred from one framework to another, rather than being uniquely attributed to a particular framework. We demonstrate this idea by examining the connection between $γ$-DDPC and the original DeePC formulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02721 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards a unifying framework for data-driven predictive control with quadratic regularization Klädtke, Manuel Darup, Moritz Schulze Systems and Control Optimization and Control Data-driven predictive control (DPC) has recently gained popularity as an alternative to model predictive control (MPC). Amidst the surge in proposed DPC frameworks, upon closer inspection, many of these frameworks are more closely related (or perhaps even equivalent) to each other than it may first appear. We argue for a more formal characterization of these relationships so that results can be freely transferred from one framework to another, rather than being uniquely attributed to a particular framework. We demonstrate this idea by examining the connection between $γ$-DDPC and the original DeePC formulation. |
| title | Towards a unifying framework for data-driven predictive control with quadratic regularization |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2404.02721 |