GA-Based Servo System Parameter Estimation during Startup

Year 2010, Volume: 2 Issue: 2, 33 - 43, 01.06.2010

A.R. Rezazade

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

• [1] Torsten Soderstorm and Petre Stoica, ”System Identification “ prentice Hall international series in systems and control engineering.
• [2] Bor-Sen Chen, Bore-Kuen Lee, and Sen-Chueh Peng,” Maximum Likelihood Parameter Estimation of F-ARIMA Processes Using the Genetic Algorithm in the Frequency Domain” IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 50, NO. 9, SEPTEMBER 2002.
• [3] Sunil L. Kukreja, Robert E. Kearney, and Henrietta L. Galiana, ”A Least-Squares Parameter Estimation Algorithm for Switched Hammerstein Systems with Applications to the VOR” IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 52, NO. 3, MARCH 2005
• [4] Benjamin C. Kuo “automatic control systems” (sixth edition), Prentice Hall, 1991
• [5] Lagarias, J.C., J. A. Reeds, M. H. Wright, and P. E. Wright, "Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions," SIAM Journal of Optimization, Vol. 9 Number 1, pp. 112147, 1998.
• [6] Ali Reza Rezazade, Arash Sayyah, Mitra Aflaki, “Optimization of THD and Suppressing Certain Order Harmonics in PWM inverters using Genetic Algorithms,” IEEE International Symposium on Intelligent Control (ISIC) October 4 to October 6, 2006, in Munich, Germany.
• [7] Arash Sayyah, Mitra Aflaki, Ali Reza Rezazade, “GA-Based Optimization of Total Harmonic Current Distortion and Suppression of Chosen Harmonics in Induction Motors,” SPEEDAM SYMPOSIUM 23-26 May 2006 - Taormina (Italy).
• [8] Ali Reza Rezazade, Arash Sayyah, Mitra Aflaki” Optimal PWM for Minimization of Total Harmonic Current Distortion in High-Power Induction Motors using Genetic Algorithms,” SICE - ICASE International Joint Conference 2006 EXCO (Busan Exhibition & Convention Center), Busan, KOREA.
• [9] A. R. Rezazade, M. Lankarani, “Parameter Estimation Optimization Based on Genetic Algorithm Applied to DC Motor.” Proceedings of ICEE 2007 International Conference on Electrical Engineering. Lahore, Pakistan on 11-12 April 2007.