Stability and Convergence of a Randomized Model Predictive Control Strategy
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913256425127936 |
|---|---|
| author | Veldman, Daniël Borkowski, Alexandra Zuazua, Enrique |
| author_facet | Veldman, Daniël Borkowski, Alexandra Zuazua, Enrique |
| contents | RBM-MPC is a computationally efficient variant of Model Predictive Control (MPC) in which the Random Batch Method (RBM) is used to speed up the finite-horizon optimal control problems at each iteration. In this paper, stability and convergence estimates are derived for RBMMPC of unconstrained linear systems. The obtained estimates are validated in a numerical example that also shows a clear computational advantage of RBM-MPC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_05463 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Stability and Convergence of a Randomized Model Predictive Control Strategy Veldman, Daniël Borkowski, Alexandra Zuazua, Enrique Optimization and Control RBM-MPC is a computationally efficient variant of Model Predictive Control (MPC) in which the Random Batch Method (RBM) is used to speed up the finite-horizon optimal control problems at each iteration. In this paper, stability and convergence estimates are derived for RBMMPC of unconstrained linear systems. The obtained estimates are validated in a numerical example that also shows a clear computational advantage of RBM-MPC. |
| title | Stability and Convergence of a Randomized Model Predictive Control Strategy |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2211.05463 |