Work place: Al-Mustansirya University/ Computer Engineering Department, Baghdad, 10001, Iraq

E-mail: engrawaa1990@gmail.com


Research Interests: Computer systems and computational processes, Artificial Intelligence, Neural Networks, Robotics, Systems Architecture


Rawaa haamed was born on sept. 22, 1990. Master of Science student, Computer Engineering dept. @ AL Mustansirya Uni. 2017, B. Sc., Computer Engineering dept. @ AL Mustansirya Uni. 2012. Interested area: Artificial Neural Networks, intelligent algorithms, Optimization Methods, Robotic system.

Author Articles
Controlling of Mean Arterial Pressure by Modified PI-ID Controller Based on Two Optimization Algorithms

By Ekhlaskaram RawaaHaamed

DOI: https://doi.org/10.5815/ijmecs.2020.04.04, Pub. Date: 8 Aug. 2020

High blood pressure is one of the diseases that most people suffer from, and it becomes a serious disease when it is not controlled precisely, especially during the surgical procedure. There must be anesthesiologists during the operation to monitor the pressure during the operation. It is not good and expensive, for patient safety and injection of the patient with the required dose, and it accurately requires an intelligent control to control the patient's pressure This paper presents nonlinear control system, to regulate the Mean Arterial Pressure (MAP) system. This controller is designed based on slate model that represent the mathematical equation that clarifies relationship between blood pressure and vasoactive drug injection. In this work Squirrel Search Algorithm (SSA) and Bacterial Foraging Optimization (BFO) are considered to optimize the controller parameters. Also nonlinear gain is used in PI-Id controller rather than fixed gain to make the controller much more sensitive to small value of error. Two algorithms applied to the controller to optimize its parameters to compare their results and determine which gives better results. The comparison results show best improvement when using the suggested controller based on SSA Algorithm. the results have no undershot with less (800s) settling time and low error.

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