The objective of this work is to optimize the performance of a constant flow parallel mechanical displacement micropump, which has parallel pump chambers and incorporates passive check valves. The critical task is to minimize the pressure pulse caused by regurgitation, which negatively impacts the constant flow rate, during the reciprocating motion when the left and right pumps interchange their role of aspiration and transfusion. Previous works attempt to solve this issue via the mechanical design of passive check valves. In this work, the novel concept of overlap time is proposed, and the issue is solved from the aspect of control theory by implementing a RBF neural network trained by both unsupervised and supervised learning. The experimental results indicate that the pressure pulse is optimized in the range of 0.15 - 0.25 MPa, which is a significant improvement compared to the maximum pump working pressure of 40 MPa.

翻译：本工作旨在优化一种具有并联泵腔并集成被动止回阀的恒流并联机械位移微泵的性能。关键任务在于最小化由回流引起的压力脉动——该脉动在左右泵交替进行吸入和输送的往复运动过程中会对恒流速率产生负面影响。以往研究尝试通过被动止回阀的机械设计解决该问题。本工作提出了重叠时间这一创新概念，并基于控制理论，通过采用无监督与有监督联合训练的RBF神经网络方法解决问题。实验结果表明，压力脉动优化至0.15-0.25 MPa区间，相较于最大泵工作压力40 MPa实现了显著改善。