Zhao Rong-Heng, Ren Shuai, Shi Yan, Cai Mao-Lin, Wang Tao, Luo Zu-Jin
School of Automation, Beijing Institute of Technology, Beijing 100081, China.
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
Bioengineering (Basel). 2025 Sep 7;12(9):963. doi: 10.3390/bioengineering12090963.
Mechanical ventilation is indispensable for patients with severe respiratory conditions, and high-fidelity lung simulators play a pivotal role in ventilator testing, clinical training, and respiratory research. However, most existing simulators are passive, single-lung models with limited and discrete control over respiratory mechanics, which constrains their ability to reproduce realistic breathing dynamics. To overcome these limitations, this study presents a dual-chamber lung simulator that can operate in both active and passive modes. The system integrates a sliding mode controller enhanced by a linear extended state observer, enabling the accurate replication of complex respiratory patterns. In active mode, the simulator allows for the precise tuning of respiratory muscle force profiles, lung compliance, and airway resistance to generate physiologically accurate flow and pressure waveforms. Notably, it can effectively simulate pathological conditions such as acute respiratory distress syndrome (ARDS) and chronic obstructive pulmonary disease (COPD) by adjusting key parameters to mimic the characteristic respiratory mechanics of these disorders. Experimental results show that the absolute flow error remains within ±3 L/min, and the response time is under 200 ms, ensuring rapid and reliable performance. In passive mode, the simulator emulates ventilator-dependent conditions, providing continuous adjustability of lung compliance from 30 to 100 mL/cmH2O and airway resistance from 2.01 to 14.67cmH2O/(L/s), with compliance deviations limited to ±5%. This design facilitates fine, continuous modulation of key respiratory parameters, making the system well-suited for evaluating ventilator performance, conducting human-machine interaction studies, and simulating pathological respiratory states.
机械通气对于患有严重呼吸疾病的患者来说不可或缺,而高保真肺模拟器在呼吸机测试、临床培训和呼吸研究中发挥着关键作用。然而,现有的大多数模拟器都是被动的单肺模型,对呼吸力学的控制有限且离散,这限制了它们再现真实呼吸动态的能力。为了克服这些限制,本研究提出了一种双腔肺模拟器,它可以在主动和被动模式下运行。该系统集成了一个由线性扩展状态观测器增强的滑模控制器,能够精确复制复杂的呼吸模式。在主动模式下,模拟器允许精确调整呼吸肌力分布、肺顺应性和气道阻力,以生成生理上准确的流量和压力波形。值得注意的是,它可以通过调整关键参数来有效模拟急性呼吸窘迫综合征(ARDS)和慢性阻塞性肺疾病(COPD)等病理状况,以模仿这些疾病的特征性呼吸力学。实验结果表明,绝对流量误差保持在±3 L/min以内,响应时间在200 ms以下,确保了快速可靠的性能。在被动模式下,模拟器模拟依赖呼吸机的状况,提供从30至100 mL/cmH2O的肺顺应性以及从2.01至14.67cmH2O/(L/s)的气道阻力的连续可调性,顺应性偏差限制在±5%以内。这种设计便于对关键呼吸参数进行精细、连续的调制,使该系统非常适合评估呼吸机性能、进行人机交互研究以及模拟病理性呼吸状态。