Precise control for the size of droplet in T-junction microfluidic based on iterative learning method

Abstract

Microfluidic droplet technique is a new technology developed on the basis of microfluidics to study the formation, manipulation and application of microdroplets of a few micrometers size. It drastically enhances the advantages of microfluidics in terms of low consumption, automation and high throughput and is widely used in chemical, microelectronics, materials science, biology and biomedical engineering etc. In this paper, an iterative learning control (ILC) scheme is proposed to accurately control the droplet size at low capillary numbers. ILC is able to revise the current control input based on the error information measured during previous experimental operations and ultimately produce the desired droplet size in the T-junction microfluidic channel we design. The feasibility of the ILC scheme is verified through experiments, where two different situations are addressed in detail. The results indicate that only a few number of iterations is required to achieve the desired droplet size. The main characteristics of the proposed ILC scheme are as follow: (1) it does not require an accurate mathematical model of the microfluidic systems, which can resolve the severe uncertainties of complex droplet microfluidic system well. (2) Owing to the feedforward characteristic and simple structure of ILC, it is easier to be implemented in practical experimental environments than typical feedback controllers, e.g., proportional-integral-derivative controller. (3) Compared with the traversal methods widely used in medical and biological fields, the idea of ILC would reduce the number of experimental trials significantly.