Data/moment-driven approaches for fast predictive control of collective dynamics
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910343118192640 |
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| author | Albi, Giacomo Bicego, Sara Herty, Michael Huang, Yuyang Kalise, Dante Segala, Chiara |
| author_facet | Albi, Giacomo Bicego, Sara Herty, Michael Huang, Yuyang Kalise, Dante Segala, Chiara |
| contents | Feedback control synthesis for large-scale particle systems is reviewed in the framework of model predictive control (MPC). The high-dimensional character of collective dynamics hampers the performance of traditional MPC algorithms based on fast online dynamic optimization at every time step. Two alternatives to MPC are proposed. First, the use of supervised learning techniques for the offline approximation of optimal feedback laws is discussed. Then, a procedure based on sequential linearization of the dynamics based on macroscopic quantities of the particle ensemble is reviewed. Both approaches circumvent the online solution of optimal control problems enabling fast, real-time, feedback synthesis for large-scale particle systems. Numerical experiments assess the performance of the proposed algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15611 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data/moment-driven approaches for fast predictive control of collective dynamics Albi, Giacomo Bicego, Sara Herty, Michael Huang, Yuyang Kalise, Dante Segala, Chiara Optimization and Control Machine Learning Multiagent Systems Feedback control synthesis for large-scale particle systems is reviewed in the framework of model predictive control (MPC). The high-dimensional character of collective dynamics hampers the performance of traditional MPC algorithms based on fast online dynamic optimization at every time step. Two alternatives to MPC are proposed. First, the use of supervised learning techniques for the offline approximation of optimal feedback laws is discussed. Then, a procedure based on sequential linearization of the dynamics based on macroscopic quantities of the particle ensemble is reviewed. Both approaches circumvent the online solution of optimal control problems enabling fast, real-time, feedback synthesis for large-scale particle systems. Numerical experiments assess the performance of the proposed algorithms. |
| title | Data/moment-driven approaches for fast predictive control of collective dynamics |
| topic | Optimization and Control Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2402.15611 |