Given the importance of high blood pressure, it is important to control and maintain a constant blood pressure level in the normal state. The main aim of this article is to design a model predictive controller with a genetic algorithm (GA) for the regulation of arterial blood pressure. The present study is an applied cross-sectional study. In order to do this research, studies related to designing mathematical models for blood pressure regulation and mechanical models for heart muscle and pressure sensors are investigated. Then, a model predictive controller with GA is designed for blood pressure control. All control and design operations are performed in the MATLAB software. According to the viscoelasticity of blood, transducer, and injection set, we can assume the mechanical model as Mass, Spring, and Damper. Initially, the patient's blood pressure is lower than normal, and after controlling, the patient's blood pressure returned to normal. By using a GA-based model predictive control (MPC), mathematical validation, and mechanical model, the patient's blood pressure can be adjusted and maintained. The simulation result shows that the GA-based MPC offers acceptable response and speed of operation and the proposed controller can achieve good tracking and disturbance rejection.

中文翻译：

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使用用于血压控制的元启发式优化模型预测控制优化。

鉴于高血压的重要性，在正常状态下控制和维持恒定的血压水平非常重要。本文的主要目的是设计一种具有遗传算法 (GA) 的模型预测控制器，用于调节动脉血压。本研究为应用横断面研究。为了进行这项研究，研究了与设计用于血压调节的数学模型和用于心肌和压力传感器的机械模型有关的研究。然后，设计了一个带有遗传算法的模型预测控制器用于血压控制。所有控制和设计操作均在 MATLAB 软件中执行。根据血液、换能器和注射装置的粘弹性，我们可以假设机械模型为质量、弹簧和阻尼器。最初，患者' 血压低于正常值，经控制后，患者血压恢复正常。通过使用基于 GA 的模型预测控制 (MPC)、数学验证和机械模型，可以调整和维持患者的血压。仿真结果表明，基于 GA 的 MPC 提供了可接受的响应和运行速度，并且所提出的控制器可以实现良好的跟踪和干扰抑制。