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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.12487 |
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| _version_ | 1866911109490933760 |
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| author | Shahbandari, Lida Mohseni, Hossein |
| author_facet | Shahbandari, Lida Mohseni, Hossein |
| contents | This study introduces a Fractional Order Fuzzy PID (FOFPID) controller that uses the Whale Optimization Algorithm (WOA) to manage the Bispectral Index (BIS), keeping it within the ideal range of forty to sixty. The FOFPID controller combines fuzzy logic for adapting to changes and fractional order dynamics for fine tuning. This allows it to adjust its control gains to handle a person's unique physiology. The WOA helps fine tune the controller's parameters, including the fractional orders and the fuzzy membership functions, which boosts its performance. Tested on models of eight different patient profiles, the FOFPID controller performed better than a standard Fractional Order PID (FOPID) controller. It achieved faster settling times, at two and a half minutes versus three point two minutes, and had a lower steady state error, at zero point five versus one point two. These outcomes show the FOFPID's excellent strength and accuracy. It offers a scalable, artificial intelligence driven solution for automated anesthesia delivery that could enhance clinical practice and improve patient results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12487 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advanced DOA Regulation with a Whale-Optimized Fractional Order Fuzzy PID Framework Shahbandari, Lida Mohseni, Hossein Artificial Intelligence Systems and Control This study introduces a Fractional Order Fuzzy PID (FOFPID) controller that uses the Whale Optimization Algorithm (WOA) to manage the Bispectral Index (BIS), keeping it within the ideal range of forty to sixty. The FOFPID controller combines fuzzy logic for adapting to changes and fractional order dynamics for fine tuning. This allows it to adjust its control gains to handle a person's unique physiology. The WOA helps fine tune the controller's parameters, including the fractional orders and the fuzzy membership functions, which boosts its performance. Tested on models of eight different patient profiles, the FOFPID controller performed better than a standard Fractional Order PID (FOPID) controller. It achieved faster settling times, at two and a half minutes versus three point two minutes, and had a lower steady state error, at zero point five versus one point two. These outcomes show the FOFPID's excellent strength and accuracy. It offers a scalable, artificial intelligence driven solution for automated anesthesia delivery that could enhance clinical practice and improve patient results. |
| title | Advanced DOA Regulation with a Whale-Optimized Fractional Order Fuzzy PID Framework |
| topic | Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2508.12487 |