基于自适应迭代优化的微压氧舱升降压速度控制方法研究

Research on Pressure Control Method for Micro-pressure Oxygen Chamber Based on Adaptive Iterative Optimization During Compression and Decompression

摘要

针对微压氧舱在升压和降压过程中因舱内压力变化导致进/排气效率非线性变化、进而引起压力变化速度不均匀并易诱发人体耳部不适的问题，提出了一种基于自适应迭代优化的升降压速度控制方法。该方法将人体耳压敏感临界值作为控制目标速度，通过动态调节电动排气阀开度以补偿压力变化率的自然衰减，并在单次运行结束后利用压力变化速率序列的均方根误差对调节系数及初始开度进行迭代修正，使系统在多次运行后逼近最优控制曲线。仿真结果表明，所提方法能够在保证压力变化速度逼近预设临界值的同时有效缩短升降压时间，且具备良好的个体化适应能力与参数自收敛特性。

Abstract

To address the problem of non-uniform pressure change rates caused by the nonlinear variation of intake/exhaust efficiency during the compression and decompression phases of micro-pressure oxygen chambers—which often leads to discomfort in the human ear—a speed control method based on adaptive iterative optimization is proposed. This method takes the individual’s critical threshold for perceiving ear pressure changes as the target control speed. By dynamically adjusting the opening of the electric exhaust valve, it compensates for the natural attenuation of the pressure change rate. After each operation cycle, the root-mean-square error of the pressure change rate sequence is used to iteratively adjust the control coefficients and the initial valve opening, enabling the system to approach the optimal control trajectory over multiple runs. Simulation results demonstrate that the proposed method effectively maintains the pressure change rate close to the preset critical threshold while significantly reducing the overall compression and decompression time, and exhibits favorable individual adaptability and parameter self-convergence characteristics.

关键词

微压氧舱 / 速度控制 / 自适应迭代优化 / 耳压敏感临界值 / 电动排气阀

Key words

micro-pressure oxygen chamber / speed control / adaptive iterative optimization / ear pressure sensitivity threshold / electric exhaust valve

参考文献

[1] 陈志刚, 李华. 微压氧疗技术在运动康复中的应用研究进展[J]. 中国运动医学杂志, 2023, 42(5): 401-406.

[2] 王丽娜, 张伟. 民用微高压氧舱的临床应用现状与展望[J]. 医疗卫生装备, 2024, 45(2): 98-103.

[3] 刘洋, 赵岩. 微压氧舱压力控制系统的设计与实现[J]. 自动化与仪表, 2022, 37(8): 62-66.

[4] 孙建军. 咽鼓管功能障碍与中耳压力调节[J]. 听力学及言语疾病杂志, 2021, 29(3): 241-245.

[5] 李明, 王磊. 基于模糊PID的微压氧舱压力控制策略研究[J]. 机械设计与制造, 2023, (11): 156-160.

[6] 张强, 陈静. 基于BP神经网络的微压氧舱PID参数自整定方法[J]. 控制工程, 2024, 31(4): 712-718.

[7] 赵敏, 周涛. 基于状态机与PID的微压氧舱复合控制策略[J]. 仪器仪表学报, 2023, 44(6): 205-213.

[8] Chen Y., Wang H. Model predictive control for pressure and oxygen concentration in mild hyperbaric chambers[J]. Biomedical Signal Processing and Control, 2025, 91: 106012.

[9] 吴迪, 徐鹏. 基于迭代优化的微压氧舱稳压段自适应控制方法: 中国, CN121143500A[P]. 2025-06-15.