Multi-level Optimal Control with Neural Surrogate Models
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866917587865042944 |
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| author | Kalise, Dante Loayza-Romero, Estefanía Morris, Kirsten A. Zhong, Zhengang |
| author_facet | Kalise, Dante Loayza-Romero, Estefanía Morris, Kirsten A. Zhong, Zhengang |
| contents | Optimal actuator and control design is studied as a multi-level optimisation problem, where the actuator design is evaluated based on the performance of the associated optimal closed loop. The evaluation of the optimal closed loop for a given actuator realisation is a computationally demanding task, for which the use of a neural network surrogate is proposed. The use of neural network surrogates to replace the lower level of the optimisation hierarchy enables the use of fast gradient-based and gradient-free consensus-based optimisation methods to determine the optimal actuator design. The effectiveness of the proposed surrogate models and optimisation methods is assessed in a test related to optimal actuator location for heat control. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07763 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-level Optimal Control with Neural Surrogate Models Kalise, Dante Loayza-Romero, Estefanía Morris, Kirsten A. Zhong, Zhengang Optimization and Control Machine Learning Numerical Analysis Optimal actuator and control design is studied as a multi-level optimisation problem, where the actuator design is evaluated based on the performance of the associated optimal closed loop. The evaluation of the optimal closed loop for a given actuator realisation is a computationally demanding task, for which the use of a neural network surrogate is proposed. The use of neural network surrogates to replace the lower level of the optimisation hierarchy enables the use of fast gradient-based and gradient-free consensus-based optimisation methods to determine the optimal actuator design. The effectiveness of the proposed surrogate models and optimisation methods is assessed in a test related to optimal actuator location for heat control. |
| title | Multi-level Optimal Control with Neural Surrogate Models |
| topic | Optimization and Control Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2402.07763 |