International Journal of Power Electronics and Drive System (IJPEDS) Vol. 11, No. 2, June 2020, pp. 1047~1054 ISSN: 2088-8694, DOI: 10.11591/ijpeds.v11.i2.pp1047-1054  1047 Journal homepage: http://ijpeds.iaescore.com Optimal parameter estimation for a DC motor using genetic algorithm Mohammad Soleimani Amiri 1 , Mohd Faisal Ibrahim 2 , Rizauddin Ramli 3 1,3 Centre for Materials Engineering and Smart Manufacturing, Universiti Kebangsaan Malaysia, Malaysia 2 Centre for Integrated Systems Engineering and Advanced Technologies, Universiti Kebangsaan Malaysia, Malaysia Article Info ABSTRACT Article history: Received Oct 1, 2019 Revised Dec 20, 2019 Accepted Jan 3, 2020 Estimating the parameters of a geared DC motor is crucial in terms of its non-linear features. In this paper, parameters of a geared DC motor are estimated genetically. Mathematical model of the DC motor is determined by Kirchhoff’s law and dynamic model of its shafts and gearbox. Parameters of the geared DC motor are initially estimated by MATLAB/Simulink. The estimated parameters are defined as initial values for Genetic Algorithm (GA) to minimize the error of the simulated and actual angular trajectory captured by an encoder. The optimal estimated model of the geared DC motor is validated by different voltages as the input of the actual DC motor and its mathematical model. The results and numerical analysis illustrate it can be ascertained that GA is appropriate to estimate the parameters of platforms with non linear characteristics. Keywords: Geared DC motor Genetic Algorithm Optimization Parameter estimation This is an open access article under the CC BY-SA license. Corresponding Author: Mohammad Soleimani Amir, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, Selangor, Malaysia. Email: p90554@siswa.ukm.edu.my 1. INTRODUCTION DC motors are used in the majority of applications around us, because of their easy operation, simple structure, and low cost [1]. For instance, a brushless DC motor has been used as an actuator for an Unmanned Aerial Vehicles (UAV) [2, 3]. In another work, Yusof et al. [4] employed a brushless DC motor in Powered Knee Orthosis (PKO) for rehabilitation purpose. Therefore, many works have been studied about various applications and different aspects of the DC motor[5-7]. Akbar et al adopted a model reference adaptive control and MIT-rule for simulating DC motor kinematic in Simscape package of MATLAB/Simulink [8]. Similarly, oy et al presented an adaptive control method based on Lyapunov function for a DC motor [9]. Furthermore, they estimated the parameters of DC motor using the adaptation law. Their proposed method was validated on simulation and compared with a conventional back stepping controller. Kumar et al. [10] implemented fuzzy logic controller for controlling speed of a Brushless DC (BLDC) motor. In another study, Somwanshi et al. [11] used fuzzy logic to tune a Proportional-Integral- Derivative (PID) controller for a DC motor. They confirmed the efficiency of the performance of their proposed controller by comparison with PID controller with a conventional tuning method. Sangsefidi et al. [12] used a four-leg converter for direct torque control (DTC) of a two-phase induction motor and hysteresis- based current control of a permanent magnet DC motor. Hoo et al. [13] presented integral anti-windup strategies of the Proportional-Integral (PI) controller for simulation of a DC motor in MATLAB/Simulink. The controller was a built-in closed-loop with single-output and multi-output, which are external torque and desired trajectory. They tested their proposed control system on load and unload condition for first