WeChat Mini Program
Old Version Features

In Vivo Performance Evaluation of an FOPID Controller for Closed-Loop Anesthesia

IEEE Transactions on Control Systems Technology(2025)

Dipartimento di Ingegneria Meccanica e Industriale

Cited 0|Views0
Abstract
In this article, we design and evaluate a fractional-order proportional-integral-derivative (FOPID) controller for the regulation of total intravenous anesthesia (TIVA). In particular, the FOPID controller has been designed to regulate the patient’s depth of hypnosis (DoH), measured via the bispectral index (BIS) monitor, by coadministrating both the hypnotic and analgesic drugs used in TIVA. Separate tunings are obtained for the induction phase and for the maintenance phase. For the former, a methodology based on the minimization of the integrated absolute error (IAE) has been employed, while for the latter, the isodamping design approach has been applied. Experiments carried out on ten patients undergoing plastic surgery show that the FOPID controller meets the clinical requirements for each one of the ten patients without requiring any manual intervention from the anesthesiologist. Notably, the controller provided a fast rejection of disturbances without provoking harmful overdosing episodes.
More
Translated text
Key words
Clinical results,closed-loop anesthesia,depth of hypnosis (DoH) control,fractional-order proportional-integral-derivative (FOPID) controller,tuning
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined