APECS: Adaptive Personalized Control System Architecture
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929757043556352 |
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| author | Juston, Marius F. R. Gisi, Alex Norris, William R. Nottage, Dustin Soylemezoglu, Ahmet |
| author_facet | Juston, Marius F. R. Gisi, Alex Norris, William R. Nottage, Dustin Soylemezoglu, Ahmet |
| contents | This paper presents the Adaptive Personalized Control System (APECS) architecture, a novel framework for human-in-the-loop control. An architecture is developed which defines appropriate constraints for the system objectives. A method for enacting Lipschitz and sector bounds on the resulting controller is derived to ensure desirable control properties. An analysis of worst-case loss functions and the optimal loss function weighting is made to implement an effective training scheme. Finally, simulations are carried out to demonstrate the effectiveness of the proposed architecture. This architecture resulted in a 4.5% performance increase compared to the human operator and 9% to an unconstrained feedforward neural network trained in the same way. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09624 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | APECS: Adaptive Personalized Control System Architecture Juston, Marius F. R. Gisi, Alex Norris, William R. Nottage, Dustin Soylemezoglu, Ahmet Systems and Control Machine Learning Robotics This paper presents the Adaptive Personalized Control System (APECS) architecture, a novel framework for human-in-the-loop control. An architecture is developed which defines appropriate constraints for the system objectives. A method for enacting Lipschitz and sector bounds on the resulting controller is derived to ensure desirable control properties. An analysis of worst-case loss functions and the optimal loss function weighting is made to implement an effective training scheme. Finally, simulations are carried out to demonstrate the effectiveness of the proposed architecture. This architecture resulted in a 4.5% performance increase compared to the human operator and 9% to an unconstrained feedforward neural network trained in the same way. |
| title | APECS: Adaptive Personalized Control System Architecture |
| topic | Systems and Control Machine Learning Robotics |
| url | https://arxiv.org/abs/2503.09624 |