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Increasing Robustness of the Anesthesia Process from Difference Patient's Delay Using a State-Space Model Predictive Controller

机译:使用状态空间模型预测控制器增加差异患者延迟麻醉过程的鲁棒性

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The process of anesthesia is nonlinear with time delay and also there are some constraints which have to be considered in calculating administrative drug dosage. We present an Extended Kalman Filter (EKF) observer to estimate drug concentration in the patient's body and use this estimation in a state-space based Model of Predictive Controller (MPC) for controlling the depth of anesthesia. Bispectral Index (BIS) is used as a patient consciousness index and propofol as an anesthetic agent. Performance evaluations of the proposed controller, the results have been compared with those of a MPC controller. The results demonstrate that state-space MPC including the EKF estimator for controlling the anesthesia process can significantly increase the robustness in encountering patients' delay deviations in comparison with the MPC.
机译:麻醉过程是非线性的,随着时间延迟,并且还存在一些限制,必须考虑计算行政药物剂量。我们提出了一个扩展的卡尔曼滤波器(EKF)观察者,以估计患者体内的药物浓度,并在用于控制麻醉深度的预测控制器(MPC)的状态空间模型中使用该估计。双光谱指数(BIS)用作患者意识指数和异丙酚作为麻醉剂。所提出的控制器的性能评估,结果与MPC控制器的结果进行了比较。结果表明,包括用于控制麻醉过程的EKF估计的状态空间MPC可以显着增加遇到患者延迟偏差的鲁棒性与MPC相比。

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