PID Controller Tuning Optimization Using Genetic Algorithm for Droplet Size Control in Microfluidics

In this paper, we demonstrate droplet size control in a microfluidics system using a PID controller tuned with a genetic algorithm (GA). In comparison to the state-of-the-art, this controller tuning method provides a robust means of obtaining controller gains that yield good steady-state responses with minimal settling time and transient overshoot. As a result, we developed a simulation of droplet microfluidics systems that is integrated with a genetic algorithm tuned-PID controller to automatically adjust the pressure of the continuous phase fluid to achieve the desired droplet sizes. When compared to the traditional tuning method, the results of the genetic algorithm demonstrated a significant improvement in process response, particularly in overshoot percentage, which decreased from 1.5 percent to 0.5 percent, and settling time, which decreased from 0.34 seconds to approximately 0.088 seconds. Using the controller gains derived from the genetic algorithm tuning, we achieved a droplet size (diameter) setpoint range of 50 to 350 micrometers. Furthermore, among the objective function criteria used in the genetic algorithm, the Integral of Time Absolute Error (ITAE) produced the best system response while having the lowest fitness value.