Genetic Algorithm based Adaptive Optimization Analysis for Nonlinear Process

Controlling the nonlinear process is a very challenging task in the process plant, whereby it is highly dependent on the knowledge and skills of the practitioners. This paper aimed at developing a Genetic Algorithm (GA) based adaptive optimization algorithm to obtain the trade-off controller tunings for the satisfactory performance of both servo and regulatory control objectives at the Low, Medium and High operating levels. The model identification and computational analysis is integrated into a Graphical User Interface (GUI) platform of MATLAB, which allows automated generation of the controller tunings for the tested models. The research is begun with empirical model identification of both process and disturbance models at the Low, Medium and High operating levels. Controller tunings are then analysed by GA algorithms and displayed on the GUI boards. The obtained controller tunings are applied to the Gravity Drained Tank function of LOOP-PRO software and its process responses and performance indexes are compared with manually calculated controller tunings. The results show that GA-based optimization analysis gives better control performance than the manually calculated controller tunings.