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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使用元启发式优化进行血压控制的模型预测控制优化。

考虑到高血压的重要性，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，以调节动脉血压。本研究是一个应用的横断面研究。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以及用于心脏肌肉和压力传感器的机械模型有关的研究。然后，设计了具有遗传算法的模型预测控制器以进行血压控制。所有控制和设计操作均在MATLAB软件中执行。根据血液，换能器和注射组的粘弹性，我们可以将机械模型假定为质量，弹簧和阻尼器。最初，病人 血压低于正常水平，经过控制后，患者的血压恢复正常。通过使用基于GA的模型预测控制（MPC），数学验证和机械模型，可以调节和维持患者的血压。仿真结果表明，基于遗传算法的MPC具有令人满意的响应速度和运行速度，并且所提出的控制器能够实现良好的跟踪和干扰抑制能力。