A neuro-fuzzy based approach for output tracking of transverse flux machines

Author

Babazadeh, A. ; Karimi, H.R. ; Moshiri, B.

Author_Institution

Inst. of Electr. Drives, Power Electron. & Devices, Bremen Univ.

fYear

2005

fDate

28-31 Aug. 2005

Firstpage

272

Lastpage

276

Abstract

This paper describes a design for adaptive control of transverse flux permanent magnet machines as nonlinear systems with unknown nonlinearities by utilizing Takagi-Sugeno-Kang type neuro-fuzzy networks. The technique of feedback linearization and H_infin control are used to design the adaptive control law for compensating the unknown nonlinear parts, such the effect of cogging torque, as a disturbance on the rotor angle and angular velocity tracking performances. Finally, the capability of the proposed method is shown by the simulation results

Keywords

adaptive control; control system synthesis; feedback; fuzzy neural nets; machine control; neurocontrollers; nonlinear systems; permanent magnet machines; tracking; H_infin control; Takagi-Sugeno-Kang type neuro-fuzzy network; adaptive control; angular velocity tracking; cogging torque; feedback linearization; neuro-fuzzy based approach; nonlinear system; output tracking; rotor angle tracking; transverse flux permanent magnet machine; Adaptive control; Angular velocity control; Forging; Fuzzy neural networks; Linear feedback control systems; Neurofeedback; Nonlinear systems; Permanent magnet machines; Takagi-Sugeno-Kang model; Torque control;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Applications, 2005. CCA 2005. Proceedings of 2005 IEEE Conference on

Conference_Location

Toronto, Ont.

Print_ISBN

0-7803-9354-6

Type

conf

DOI

10.1109/CCA.2005.1507137

Filename

1507137