Wang Leijie, Guo Xudong, Peng Qiuyue, Zhang Hongmei, Yang Yuan, Wang Hongyan, Wang Yongxin, Liang Haofang, Ming Wuyi, Zhang Zhen
School of Mechanical Engineering, Dongguan University of Technology, Dongguan, China.
Mechanical and Electrical Engineering Institute, Zhengzhou University of Light Industry, Zhengzhou, China.
PLoS One. 2025 Jun 4;20(6):e0324261. doi: 10.1371/journal.pone.0324261. eCollection 2025.
In order to further improve the injection precision of the PH300 insulin pump, this paper optimizes and improves the mechanical structure and control algorithm of the PH300. The improved PH300 uses a proportional-integral-derivative controller based on back propagation neural network (BP-PID) algorithm to control operation, and the experimental results show that the minimum effective single infusion dose of the improved PH300 is 0.047 U, which is reduced by 50.52%. The deviation reduction of low-dose infusion (0.1U-0.9U) ranged from 1.47% to 10.87%, with a mean of 4.91%. The mean deviation of the improved PH300 decreases by 12.85% after a 24h low basal rate (0.5U/h) injection. In addition, Long Short-Term Memory (LSTM) was used to predict the deviation during injection, and the predicted values were uniformly compensated for in subsequent injection experiments. The LSTM model performed best with a training set of 85%, a test set of 15%, an epoch of 300, a batch number of 256, and 32 hidden layer neurons. After compensation, the mean infusion deviation for large doses was reduced by 12.05%, and the maximum deviation by 14.12%.
为进一步提高PH300胰岛素泵的注射精度，本文对PH300的机械结构和控制算法进行了优化改进。改进后的PH300采用基于反向传播神经网络（BP-PID）算法的比例积分微分控制器来控制运行，实验结果表明，改进后的PH300最小有效单次输注剂量为0.047U，降低了50.52%。低剂量输注（0.1U-0.9U）的偏差降低范围为1.47%至10.87%，平均为4.91%。在进行24小时低基础率（0.5U/h）注射后，改进后的PH300平均偏差降低了12.85%。此外，使用长短期记忆网络（LSTM）预测注射过程中的偏差，并在后续注射实验中对预测值进行统一补偿。当训练集为85%、测试集为15%、轮次为300、批量为256且隐藏层神经元为32个时，LSTM模型表现最佳。补偿后，大剂量输注的平均偏差降低了12.05%，最大偏差降低了14.12%。
