The Optimization of the Constant Flow Parallel Micropump Using RBF Neural Network

TL;DR

This paper presents a novel control-based optimization of a constant flow parallel micropump using an RBF neural network to minimize pressure pulses, improving performance beyond traditional mechanical design methods.

Contribution

It introduces the concept of overlap time and applies an RBF neural network trained with combined learning methods for micropump optimization.

Findings

Pressure pulse optimized to 0.15-0.25 MPa

Significant improvement over traditional methods

Neural network effectively controls pump performance

Abstract

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.