基于模型的闭环MCPS设计与仿真  

Design and simulation of model-based closed-loop MCPS

作　　者：刘达阳 张立臣[1] Liu Dayang;Zhang Lichen(School of Computer,Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学计算机学院,广州510006

出　　处：《电子测量技术》2022年第13期46-53,共8页Electronic Measurement Technology

基　　金：国家自然科学基金(61873068)项目资助。

摘　　要：针对患者间的差异导致医疗闭环控制系统具有不确定性的问题,基于MCPS的概念提出一种基于模型的闭环自适应控制体系架构,以BIS信号为控制变量来控制麻醉中的催眠深度。控制方案使用患者的药代动力学/药效学模型,将计算结果作为标准PID控制器的反馈信号来修正模型的不确定性。模型中,参数的调整使用遗传算法离线执行,以此最优化患者数据集的性能指标。提出的方法可以自动调节麻醉药物的输注速率,使麻醉深度保持在一个稳定的目标值。对该方法进行了仿真模拟,加入了噪声块进行了鲁棒性测试,采用蒙特卡洛方法验证了该方法在广泛人群上的有效性。仿真结果表明,该方法能够在规定时间内稳定地达到目标值,且具有良好的干扰排斥反应。Focus on the uncertainty of medical closed-loop control system caused by differences among patients,a model-based closed-loop adaptive control architecture was proposed based on the concept of MCPS.BIS signal was used as the control variable to control the depth of hypnosis during anesthesia.The pharmacokinetic and pharmacodynamic model of the patient was used in the control scheme,and the calculated results were used as the feedback signal of the standard PID controller to correct the uncertainty of the model.In the model,parameter adjustment is performed offline using genetic algorithm to optimize the performance indicators of the patient data set.The infusion rate of anesthetic drugs can be automatically adjusted to keep the depth of anesthesia at a stable target value.The robustness of the proposed method was tested by adding noise blocks.Monte Carlo method was used to verify the effectiveness of the proposed method on a wide range of people.Simulation results show that the proposed method can reach the target value stably in a specified time and has good interference rejection.

关 键 词：医疗信息物理系统 闭环系统 麻醉 仿真 自适应控制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]