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针对麻醉用药的个体差异,术中麻醉维持的复杂性和不确定性,以及当前监测手段存在的缺陷,传统麻醉深度PID控制器不能满足其非线性控制需要,而以往麻醉深度(DOA)模糊控制器的规则完全依赖于经验调节,因此无法达到预期的控制效果。本研究建立了以大脑状态指数(CSI)为反馈变量的模糊麻醉闭环控制系统,并采用粒子群算法同时优化基于CSI的变化和异丙酚输出率之间的模糊控制规则和隶属度函数。通过系统仿真将CSI值的目标设定在40和30,并加入高斯噪声以模拟临床干扰。实验表明,该系统能准确、快速、平稳地达到CSI预设值,且在噪声干扰下,无明显扰动。经粒子群优化(PSO)过的基于CSI模糊控制器应用在DOA闭环控制系统具有较好的稳定性及鲁棒性。
Traditional anesthesia depth PID controller can not meet the needs of its nonlinear control for the individual differences in narcotic medication, the complexity and uncertainty of intraoperative anesthesia maintenance, and the defects of the current monitoring methods. However, the conventional fuzzy control of depth of anesthesia (DOA) The rules of the device are entirely dependent on empirical adjustments and therefore do not achieve the expected control effect. In this study, a fuzzy closed-loop anesthesia closed-loop control system with CSI as the feedback variable was established. Particle swarm optimization was used to optimize the fuzzy control rules and membership functions between CSI-based changes and the output rate of propofol. The goal of CSI values was set at 40 and 30 by system simulation, and Gaussian noise was added to simulate clinical interference. Experiments show that the system can accurately, quickly and smoothly reach the CSI preset value, and no significant disturbance under the noise interference. The particle swarm optimization (PSO) -based CSI fuzzy controller has good stability and robustness in DOA closed-loop control system.