Learning-Based Efficient Approximation of Data-Enabled 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_ | 1866909311149539328 |
|---|---|
| author | Zhou, Yihan Lu, Yiwen Li, Zishuo Yan, Jiaqi Mo, Yilin |
| author_facet | Zhou, Yihan Lu, Yiwen Li, Zishuo Yan, Jiaqi Mo, Yilin |
| contents | Data-Enabled Predictive Control (DeePC) bypasses the need for system identification by directly leveraging raw data to formulate optimal control policies. However, the size of the optimization problem in DeePC grows linearly with respect to the data size, which prohibits its application to resource-constrained systems due to high computational costs. In this paper, we propose an efficient approximation of DeePC, whose size is invariant with respect to the amount of data collected, via differentiable convex programming. Specifically, the optimization problem in DeePC is decomposed into two parts: a control objective and a scoring function that evaluates the likelihood of a guessed I/O sequence, the latter of which is approximated with a size-invariant learned optimization problem. The proposed method is validated through numerical simulations on a quadruple tank system, illustrating that the learned controller can reduce the computational time of DeePC by a factor of 5 while maintaining its control performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16727 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning-Based Efficient Approximation of Data-Enabled Predictive Control Zhou, Yihan Lu, Yiwen Li, Zishuo Yan, Jiaqi Mo, Yilin Systems and Control Data-Enabled Predictive Control (DeePC) bypasses the need for system identification by directly leveraging raw data to formulate optimal control policies. However, the size of the optimization problem in DeePC grows linearly with respect to the data size, which prohibits its application to resource-constrained systems due to high computational costs. In this paper, we propose an efficient approximation of DeePC, whose size is invariant with respect to the amount of data collected, via differentiable convex programming. Specifically, the optimization problem in DeePC is decomposed into two parts: a control objective and a scoring function that evaluates the likelihood of a guessed I/O sequence, the latter of which is approximated with a size-invariant learned optimization problem. The proposed method is validated through numerical simulations on a quadruple tank system, illustrating that the learned controller can reduce the computational time of DeePC by a factor of 5 while maintaining its control performance. |
| title | Learning-Based Efficient Approximation of Data-Enabled Predictive Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2404.16727 |