Identification of DC Motor Parameters using the ZOAMetaheuristic Algorithm with comparative study

Abstract

In this work, the parameters of the direct current motor (DC motor) were estimated using its armature current and speed responses, employing a recent optimization algorithm known as the Zebra Optimization Algorithm (ZOA). ZOA is recognized for its high accuracy and fast convergence, as demonstrated in various recent studies. To evaluate its effectiveness, ZOA was applied to estimate the DC motor parameters and its performance was compared with two well-known algorithms: the Enhanced Opposition-Based Equilibrium Optimizer-Slim Mold Algorithm (EOSMA) and the Grey Wolf Optimizer (GWO). The results show that ZOA outperforms both EOSMA and GWO in minimizing theobjective function, which reflects the estimation error of both armature current and speed. These findings confirm the superiority of ZOA in accurately identifying DC motor parameters.