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：本文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式微泵的性能。关键任务在于最小化因回流引起的压力脉冲（该脉冲对恒流速率产生负面影响），发生在左右泵交替执行抽吸与输注功能的往复运动过程中。以往的研究试图通过被动止回阀的机械设计来解决此问题。本文提出了重叠时间这一创新概念，并从控制理论角度出发，通过实施结合无监督学习与有监督学习的RBF神经网络来解决问题。实验结果表明，压力脉冲被优化至0.15-0.25 MPa范围内，相较于最大泵工作压力40 MPa，取得了显著改善。