Parameter Estimation Optimization Based on Genetic Algorithm Applied to DC Motor

Abstract

Thin paper proposed the application of genetic algorithm optimization in estimating the parameters of dynamic state of DC motor. LSE estimation is considered as a convenient method for parameter estimation, in comparison with this proposed method. Despite of LSE estimation that is based on the linearity of error function due to parameters, GA method can easily identify unknown parameters by minimizing the sum of squared errors. GA is imported in comparison with conventional optimization methods because of its power in searching entire solution space with more probability of finding the global optimum. Also the model can be nonlinear with respect to parameters, and in this identification free noise system is assumed and transient excitation is considered instead of persistent excitation. Finally comparison between LSE and GA optimization is presented to indicate robustness and resolution of GA identification method in parameter estimation.