The Optimization of the Constant Flow Parallel Micropump Using RBF Neural Network
This work addresses pressure pulse optimization in micropumps for applications requiring precise fluid control, representing an incremental improvement over prior mechanical design approaches.
The paper tackled the problem of minimizing pressure pulses in a constant flow parallel micropump, which disrupt flow rate, by proposing an overlap time concept and using an RBF neural network for control, resulting in optimized pressure pulses in the range of 0.15-0.25 MPa compared to a maximum working pressure of 40 MPa.
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.