A COVID-19 Based on Fractional Order Integral-Tilt Derivative Controller for Nonlinear Servomechanism Model

Abstract

In this research, a new one-axis servomechanism investigation is presented, taking into account parameter fluctuation and system uncertainty. Additionally, a novel method for very effective TID control was created to accurately monitor a chosen profile. A comparative study between the suggested control method and the well-known controllers (PID and Nonlinear PID) is also included. The COVID-19 optimization technique was used to discover the best control parameters. Through the online simulation, the servomechanism system's settings were modified at random within a predetermined range. As nonlinearity resources (friction, backlash, environmental influences), these parameters fluctuate and contribute to system uncertainty. It had been completed and looked at to compare the linear and nonlinear models. The results demonstrate that the suggested controller is capable of tracking the number of operational points with high accuracy, a short rising time, and little overrun.