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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| Online-Zugang: | https://arxiv.org/abs/2211.05463 |
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| _version_ | 1866913256425127936 |
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| 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 |