Equilibrium Optimizer Based FOPID Control of BLDC Motor

Abstract

The main challenges of proportional integral derivative (PID) control are sudden set-point changes and parameter changes, which leads to poor response. It can be taken into account that this control unit can be replaced by another similar control unit, but it differs from it in the degree of integration and differentiation, and this is what is known as fractional-order PID (FOPID), which improves the performance of the system in the transient state. To choose the FOPID constants, various methodologies, including optimization algorithms, are used to obtain the best possible performance. In this paper, the speed of brushless DC motor (BLDC) was regulated using (FOPID), where the equilibrium optimizer (EO) algorithm was used to find the values of the controller constants, and the performance of this algorithm was compared with several other optimization algorithms such as particle swarm optimization (PSO), differential evolution (DE), and golden jackal optimization (GJO). Simulation results in Matlab-Simulink 2016a showed the effectiveness of the proposed algorithm (EO) in achieving response time, overshot, and lower steady state error compared with the rest of the algorithms.