A method for controlling an ultrasonic actuated pump lung based on reinforcement learning and fuzzy control
By employing a method of ultrasound-driven pump-lung control based on reinforcement learning and fuzzy control, the pump-lung pulsation cycle and blood flow are dynamically adjusted, solving the problems of difficulty in dynamically adjusting oxygenation efficiency and device complexity in existing technologies, and achieving efficient and intelligent blood circulation support.
CN119846956BActive Publication Date: 2025-12-12SHANGHAI JIAOTONG UNIV
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Patent Information
- Application Number
- CN202411908252.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-23
- Publication Date
- 2025-12-12
- Estimated Expiration
- 2044-12-23
AI Technical Summary
Technical Problem
Existing technologies for improving oxygenation efficiency are difficult to dynamically adjust, and the equipment systems are relatively complex.
Method used
An ultrasound-driven lung pump control method based on reinforcement learning and fuzzy control is adopted. By constructing a fuzzy controller and a deep reinforcement learning model, the pump pulsation cycle and blood flow are dynamically adjusted. Combined with the precise control of the ultrasound motor, the blood flow pattern is optimized.
Benefits of technology
It significantly improves oxygenation efficiency, adapts to dynamic optimization under different working conditions, reduces blood damage, and achieves intelligent control and efficient blood circulation support for the device.
✦ Generated by Eureka AI based on patent content.
Abstract
The application discloses a kind of based on reinforcement learning and fuzzy control's ultrasonic actuator pump lung control method, it is related to extracorporeal membrane oxygenation field, including the following steps: according to expert system knowledge, construct the fuzzy rule between basic vital signs and pulsation frequency, obtain fuzzy controller, adjust average flow;According to the flow field condition of oxygenation device, construct the interactive environment of agent;The state space and action space of agent control strategy are constructed, and the movement of motor is kept stable;Establish and train the blood pump control strategy model based on deep reinforcement learning, obtain blood flow waveform curve and blood pump control strategy;Intelligent control is carried out to motor by extracting trained model, and the control performance of strategy model is evaluated.The application finds optimal flow waveform using deep reinforcement learning, regulates and controls blood flow field, significantly improves oxygenation efficiency without complex oxygenator design conditions, and different agents can be obtained according to different oxygenator structures, with strong portability.
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