Proposing a feedback-feedforward neuro-fuzzy-PID controller to produce precise micro-droplets in a flow-focusing micro-channel

In this paper, a novel feedback-feedforward structure is proposed for precise droplet generation. An Adaptive Neuro-Fuzzy Inference System is used as an inverse model to predict the desired motor speed in the feedforward path, and a PID controller to compensate for the disturbances in the feedback path. The Neuro-Fuzzy model computes the desired droplet size based on the desired droplet size and flow characteristics. A flow-focusing microchannel is constructed using photolithography to generate micro-droplets. DI water as the discrete phase and oil as the continuous phase are mixed with accurate flow rates. The DI water flow rate is kept fixed and the flow rate of oil is controlled using the proposed controller. In the feedback loop, the micro droplets' sizes are measured with an image processing of pictures captured with a high-speed camera. The performance of the closed-loop system is implemented experimentally in different desired sizes i.e., 82, 90, and 100 micrometers. It is shown that the performance of the system i.e., rise time, settling time, and accuracy is better in larger sizes. Moreover, due to the inverse neuro-fuzzy model in the feedforward loop, the rise time became small. The performance of the system in dealing with disturbances i.e., a 50% increase in discrete phase flow (water), has been considered. The results show that the closed-loop system is fast and robust against disturbances.