Multiobjective genetic estimation of DC motor parameters and load torque

Abstract

In order to simplify the offline identification of motor parameters, a new method based on optimization using a multiobjective elitist genetic algorithm is proposed. The non-dominated sorting genetic algorithm (NSGA-II) is used to minimize the error between the current and velocity responses of data and an estimated model. The robustness of the method is shown by estimating parameters of a DC motor in four different cases. Simulation results show that the method successfully estimates the motor parameters and is also capable of identifying a load torque simultaneously.