Extended Luenberger Observer Based on Dynamic Neural Network for Inertia Identification in PMSM Servo System

Author

Cao, Xianqing ; Bi, Meng

Author_Institution

Coll. of Inf. Eng., Shenyang Inst. of Chem. Technol., Shenyang, China

Volume

2

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

48

Lastpage

52

Abstract

A new scheme to estimate the moment of inertia in the motor drive system in very low speed is proposed. The simple speed estimation scheme, which is used in most servo systems for low-speed operation, is sensitivity to variations in machine parameters especially the moment of inertia. To estimate the motor inertia value, an extended Luenberger observer (ELO) is applied. The observer gain matrix can be adjusted on-line based on dynamic neural network. The effectiveness of the proposed ELO is verified by simulation results.

Keywords

neural nets; permanent magnet motors; servomotors; synchronous motors; PMSM servo system; dynamic neural network; estimation scheme; extended Luenberger observer; inertia identification; moment of inertia; motor drive system; motor inertia value; observer gain matrix; Artificial neural networks; Chemical technology; Least squares approximation; Neural networks; Nonlinear systems; Recurrent neural networks; Sampling methods; Servomechanisms; Servomotors; Torque; dynamic neural network; extended Luenberger observer; inertia estimation; servo system;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2009. ICNC '09. Fifth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3736-8

Type

conf

DOI

10.1109/ICNC.2009.357

Filename

5364709