Genetic Algorithm based Servo System Parameter Estimation during Transients

The application of Genetic Optimization Algorithm in estimation of the parameters of servo electrical drives is proposed. In comparison with this planned method, least squared error (LSE) estimation method is considered as an expedient method for parameter estimation. Regardless of LSE estimation, Genetic Algorithm method is not restricted to the linear systems with respect to the parameters. GA is imported as an optimization method in comparison with conventional optimization methods because of its power in searching whole solution space with more probability to finding the global optimum. As a condition for convergence, transient excitation is considered instead of persistent excitation. Finally, comparison between LSE and GA based parameter estimation is presented to indicate robustness and resolution of GA identification method. It will be shown that the GA method of estimation has better results in the startup and transients of the system where there is a lack of persistent excitation.