A Robust Model Predictive Control Approach to Intelligent Respiratory Support
A Robust Model Predictive Control Approach to Intelligent Respiratory Support
复制标题
智能呼吸支持的鲁棒模型预测控制方法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
P. Rostalski
中科院分区：
文献类型：
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作者：
G. Männel;C. Hoffmann;P. Rostalski
呼吸支持是几乎所有重症监护治疗的主要组成部分之一。一方面提供足够的氧合和去除二氧化碳，同时防止或减少呼吸机引起的肺损伤之间的权衡是任何呼吸治疗师的关键挑战。现代医疗器械中有几个决策支持系统可用于支持临床医生做出这些具有挑战性的决策，其中安全性是核心要求。在这种情况下，本论文的目的是通过提出一个强大的模型预测控制器（MPC），以实现足够的气体交换，通过调整每分钟通气量在安全的生理限制的领域作出贡献。在一个强大的MPC方法，保证潮气末二氧化碳的分压的演变，尽管一个未知的，但有界的代谢生产率和双线性动力学的生理非线性二室患者模型中使用。后者被认为是附加的干扰和拒绝的一个额外的反馈控制器。基于生理模型的仿真结果验证了该方法的适用性。
Respiratory support is one of the main components of almost any intensive care therapy. The trade-off between providing adequate oxygenation and removing carbon dioxide on the one hand while at the same time preventing or reducing ventilator-induced lung injuries is a key challenge for any respiratory therapist. Several decision support systems are available in modern medical ventilators to support clinicians with these challenging decisions, where safety is a core requirement. In this context, the present paper aims to contribute to the field by proposing a robust model predictive controller (MPC) to achieve adequate gas exchange by adjusting the minute volume ventilation within safe physiological limits. A physiological nonlinear two-compartment patient model is used in a robust MPC approach, which guarantees the evolution of the partial pressure of end-tidal carbon dioxide despite an unknown but bounded metabolic production rate and the bilinear dynamics. The latter are considered as additive disturbances and rejected by an additional feedback controller. Simulation results based on a physiological model exemplifies the applicability of the proposed approach.