Future Technology Recent Articles

Optimized PID control for automated blood pressure management in post-operative care

Paper Title: Optimized PID control for automated blood pressure management in post-operative care

Authors: Jegatheesh Anbazhagan, Siddheswar Kar, Krishna Prakash Arunachalam, Aravinda Koithyar

Corresponding Author: Jegatheesh Anbazhagan (jegatheeha@gmail.com), India

 

Abstract

Maintaining optimal Blood Pressure (BP) is vital, as abnormal BP levels pose substantial challenges to patient recovery in post-operative care. The manual administration of Sodium Nitroprusside (SNP) is a common approach to lower BP by relaxing peripheral vascular smooth muscles. Nevertheless, because of the inconsistency in drug sensitivity between patients, manual dosing is inaccurate and labour-intensive as it necessitates continuous expert monitoring. Therefore, this research adapts a control method to regulate BP in post-operative patients with hypertension. The Prairie Dog Optimization-based Proportional-Integral-Derivative (PDO-PID) controller adapts in real-time to the particular physiological responses of the patients, assuring precise and individualized SNP dosing. According to simulation results, the controller effectively controls BP levels over an extended time, generating an execution time of 63.613s and a reduced settling time of 1.05s. Corresponding SNP infusion levels are also effectively regulated, which is significantly smaller than the previous control approaches.

Keywords

Blood pressure, Sodium nitroprusside, Prairie dog optimization, Proportional-integral-derivative controller,Infusion pump

 

Cite:

Anbazhagan, J., Kar , S. ., Krishna Prakash Arunachalam, & Koithyar, A. . (2025). Optimized PID control for automated blood pressure management in post-operative care. Future Technology4(3), 10–18. Retrieved from https://fupubco.com/futech/article/view/294

Related posts

Hybrid boost-cuk converter with bat-chicken swarm-optimized PI controller for photovoltaic grid systems

admin

A unified machine learning framework for enterprise portfolio forecasting

admin

Research on real-time data display and production management in a digitalized management factory

admin

Leave a Comment