Fast and memory-efficient optimization for large-scale data-driven predictive control
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_ | 1866913237979627520 |
|---|---|
| author | Schmitz, Philipp Schaller, Manuel Voigt, Matthias Worthmann, Karl |
| author_facet | Schmitz, Philipp Schaller, Manuel Voigt, Matthias Worthmann, Karl |
| contents | Recently, data-enabled predictive control (DeePC) schemes based on Willems' fundamental lemma have attracted considerable attention. At the core are computations using Hankel-like matrices and their connection to the concept of persistency of excitation. We propose an iterative solver for the underlying data-driven optimal control problems resulting from linear discrete-time systems. To this end, we apply factorizations based on the discrete Fourier transform of the Hankel-like matrices, which enable fast and memory-efficient computations. To take advantage of this factorization in an optimal control solver and to reduce the effect of inherent bad conditioning of the Hankel-like matrices, we propose an augmented Lagrangian lBFGS-method. We illustrate the performance of our method by means of a numerical study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13090 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast and memory-efficient optimization for large-scale data-driven predictive control Schmitz, Philipp Schaller, Manuel Voigt, Matthias Worthmann, Karl Optimization and Control Recently, data-enabled predictive control (DeePC) schemes based on Willems' fundamental lemma have attracted considerable attention. At the core are computations using Hankel-like matrices and their connection to the concept of persistency of excitation. We propose an iterative solver for the underlying data-driven optimal control problems resulting from linear discrete-time systems. To this end, we apply factorizations based on the discrete Fourier transform of the Hankel-like matrices, which enable fast and memory-efficient computations. To take advantage of this factorization in an optimal control solver and to reduce the effect of inherent bad conditioning of the Hankel-like matrices, we propose an augmented Lagrangian lBFGS-method. We illustrate the performance of our method by means of a numerical study. |
| title | Fast and memory-efficient optimization for large-scale data-driven predictive control |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2402.13090 |