Optimization Performance Integral Criteria Based on Hybrid Soft Computing for QTS System

Abstract

This research work proposed MPSO methods for nonlinear complex QTS process. This system is implemented in various process control industries, design, and development of a new controller to increase the better stability and improve the performance of integral criteria. This proposed work goal is to minimize parameters for process controller by statistical Taguchi method combined with mutation particle swarm optimization algorithm for industrial laboratory highly complex nonlinear QTS. The designed value is tested using Simulink model in MATLAB. Using proposed controller values are tested in the real experiment set up and experiment output response is reached. The various controller designed are PID controller for QTS. The result shows that TMPSO technique is provided the good result when compared with other approaches. The TMPSO techniques use for setting controller offers enhanced process specification such as better time domain specifications, smooth error reference tracking, and minimization of error in the nonlinear system.