Fuzzy type two self-tuning technique of single neuron PID controller for brushless DC motor based on a COVID-19 optimization

Abstract

This paper presents an efficient interval type 2 fuzzy (IT2F) based on a single neuron proportional–integral–derivative (PID), also known as IT2FSNPID controller. The main purpose of the proposed control technique is to track the motion profile of the brushless DC (BLDC) motor. Also, a comparative study was investigated fuzzy type 1 (FT1) and IT2F. IT2F can treat the uncertainty and nonlinearity of the BLDC motor drive electric system in contrast to FT1. The parameters of each control technique were obtained using a new COVID-19 optimization algorithm according to an objective function. Moreover, several tests had been performed to ensure the ability of fuzzy type to absorb the system uncertainty and nonlinearity. All controllers were utilized to operate the BLDC motor sudden change in load and continuous load. The simulation results show that the IT2FSNPID can improve the dynamic response of linear and nonlinear of the same BLDC motor and accommodate the system uncertainty significantly.