Studies on various applications using multi objective optimization approaches for tuning pid controllers

Abstract

PID controller is widely utilized in numerous industrial applications newlinefor the purpose of regulating the process variables. These controllers employ a newlinecontrol loop feedback mechanism for controlling the process variables. Though newlinethe utilization of PID controllers has a great impact on industries, it suffers from newlineseveral drawbacks as well.The PID controllers cannot respond well for the immediate change newlinein load or error measurement and its speed overshoot is unfavorable. The newlineperformance of the PID controller strongly relies on the system model and newlinethe parameters. Hence, the real-time applications can make the best use of newlinePID controllers upon addressing all the aforementioned challenges. One of the newlinecrucial challenges confronted by PID controller is the PID tuning. newlineRecently, a survey reported that over eighty percent of industries newlineutilize poorly tuned PID controllers. Additionally, some controllers are based on newlinemanual mode of operation, while some exploit the default settings made by the newlinePID manufacturers. The PID tuning has a great impact on the performance of newlinethe PID controller. Hence, several approaches are proposed in the literature for newlineeffective PID tuning and some of the noteworthy techniques are Ziegler-Nichols newline(Z-N), Cohen-Coon (C-C) technique and so on. However, the utilization of newlinethese techniques leads to certain other drawbacks. For instance, incorporation newlineof lump-sum rules, incompetent dynamics due to the tuning of closed loop newlineresponses and mathematical complexities. In order to tackle these issues, this research work proposes to employ soft computing algorithms for better tuning of the PID controllers. In the process of industrial control, the PID controller may undergo several unexpected scenarios, yet the PID tuning must be done in line with the nature of application. The PID tuning focuses to choose the perfect optimal parameters to attain tuning. These parameters are then formed as function called objective function. Several optimization algorithms such as Genetic Algorithm (GA), Particle Swarm