Development of an ILC controller for the Droplet-on-Demand generation system

Abstract

The droplet-on-demand systems have been increasingly attracting attention from many research groups. The droplet-on- demand system is a promising platform for many biochemical applications, especially for drug delivery applications, where the matter quantity requires very high accuracy, in microliter- scale or even in nanoliter-scale, for enhancing the efficiency, reducing the toxicity as well as the adverse effects of medicines. In order to generate microdroplets, microfluidic technology is emerged as one of the most effective methods. In this paper, we propose a droplet-on-demand generation system based on the Y-junction microfluidic structure with the generated droplet sizes are adjusted by iterative learning control (ILC) technique. The ILC controller can modify the current control input based on the error information measured during earlier experimental procedures, resulting in the desired droplet size in the proposed Y-junction microfluidic channel. The effectiveness of the ILC method was validated by experiments. The obtained results show that the proposed system can generate the required droplet size after 6 to 7 iterations. The results also prove the potential of integrating the ILC technique in the microdroplet generation system. The propose system can be extended to apply in the drug delivery systems for patients in hospitals or chemical mixing systems.