# Advanced DOA Regulation with a Whale-Optimized Fractional Order Fuzzy PID Framework

arXiv: https://arxiv.org/abs/2508.12487 · HTML: https://mlsanity.com/paper/2508.12487/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 17 Aug 2025.

## Builds on (most cited first, 5 of 5)

- [Multimodal Object Detection using Depth and Image Data for Manufacturing Parts](https://mlsanity.com/paper/2411.09062/) (2024)
- [Concrete Surface Crack Detection with Convolutional-based Deep Learning Models](https://mlsanity.com/paper/2401.07124/) (2024)
- [Optimal Inferential Control of Convolutional Neural Networks](https://mlsanity.com/paper/2410.09663/) (2024)
- [Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications](https://mlsanity.com/paper/2504.16972/) (2025)
- [Optimizing Flamelet Generated Manifold Models: A Machine Learning Performance Study](https://mlsanity.com/paper/2507.01030/) (2025)

## Abstract (arXiv)

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.