Soft Computing Based Controllers Implementation for Non-linear Process in Real Time

The aim of this paper is to implement controllers based on soft computing techniques in real time for a non-linear process. The process taken up for study is to control the level in a conical tank setup using cost effective data acquisition system. The system identification of this nonlinear process is done using black box modeling and found to be First Order Plus Dead Time (FOPDT) model. Then the controller tuning strategy has been applied using Skogestad’s PI tuning method. The soft computing based controllers like Fuzzy Logic Controller (FLC) and Genetic Algorithm (GA) based controller has been implemented and compared with conventional PI tuning method. From the results based on Performance indices like Integral Absolute Error (IAE) and Integral Squared Error (ISE), it is proved the controllers implemented using soft computing techniques out performs well than the conventional controller.