GA-Based Servo System Parameter Estimation during Startup

Abstract

This paper proposed the application of Genetic Optimization Algorithm in estimation of the parameters of servo electrical drives. In comparison with this proposed method, least squared error (LSE) estimation method is considered as a convenient method for parameter estimation. Despite of LSE estimation, GA method is not restricted to the linear systems respect to the. GA is imported as an optimization method in comparison with conventional optimization methods because of its power in searching entire solution space with more probability of 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 have better results in the start up of the system where there is a lack of persistent excitation

Keywords

• Parameter Estimation, startup, Genetic optimization, Least Square Error Estimation, System Identification, Servo drive

References

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