Switched Reluctance Motor Drive for Electric Motorcycle Using HFNN Controller

Author

Lin, Chih-Hong

Author_Institution

Nat. United Univ., Miaoli

fYear

2007

fDate

27-30 Nov. 2007

Firstpage

1383

Lastpage

1388

Abstract

The switched reluctance motor (SRM) drive system using hybrid fuzzy neural network (HFNN) controller is developed to control electric motorcycle in this paper. First, the dynamic models of a SRM drive system and electric motorcycle are builted though experimental tests and parameters measurements. Then, a HFNN speed control system that combined supervisor control, FNN control and compensated control is developed to control SRM drive system in order to drive electric motorcycle. In the proposed HFNN control scheme, an optimum phase advancing is achieved by continuous adaptation of the optimum conduction position for the FNN. The electric motorcycle is operated to provide constant disturbance torque. Finally, the effectiveness of the proposed control schemes is demonstrated by experimental results.

Keywords

fuzzy neural nets; machine vector control; motorcycles; reluctance motor drives; HFNN controller; SRM drive system; electric motorcycle; hybrid fuzzy neural network; switched reluctance motor drive; Control systems; Electric variables measurement; Fuzzy control; Fuzzy neural networks; Motorcycles; Reluctance machines; Reluctance motors; System testing; Torque; Velocity control;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Electronics and Drive Systems, 2007. PEDS '07. 7th International Conference on

Conference_Location

Bangkok

Print_ISBN

978-1-4244-0645-6

Electronic_ISBN

978-1-4244-0645-6

Type

conf

DOI

10.1109/PEDS.2007.4487885

Filename

4487885