Robust output tracking of transverse flux machines using RBF neural network

Author

Karimi, H.R. ; Babazadeh, A. ; Parspour, N.

Author_Institution

Dept. of Electr. & Comput. Eng., Tehran Univ., Iran

Volume

1

fYear

2004

fDate

1-3 Dec. 2004

Firstpage

496

Abstract

This paper presents an application of radial basis function (RBF) in the identification and control design of transverse flux machines as nonlinear systems with unknown nonlinearity part. The technique of feedback linearization and H_∞ control are used to design an adaptive control law for compensating the unknown nonlinearity part, such that the effect of the cogging torque as a disturbance is decreased into the angle and angular velocity tracking.

Keywords

H^∞ control; adaptive control; control nonlinearities; electric machines; feedback; identification; linearisation techniques; machine control; neurocontrollers; nonlinear control systems; radial basis function networks; robust control; torque control; H_∞ control; RBF neural network; adaptive control law; angle tracking; angular velocity tracking; cogging torque; control design; feedback linearization; identification; nonlinear systems; radial basis function; robust output tracking; transverse flux machines; unknown nonlinearity; Adaptive control; Angular velocity control; Control design; Forging; Linear feedback control systems; Neural networks; Neurofeedback; Nonlinear systems; Robustness; Torque control;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics, Automation and Mechatronics, 2004 IEEE Conference on

Print_ISBN

0-7803-8645-0

Type

conf

DOI

10.1109/RAMECH.2004.1438970

Filename

1438970