A New Recurrent Fuzzy Neural Network Sliding Mode Position Controller Based on Vector Control of PMLSM Using SVM

Author

Yang, Junyou ; Chen, Ruijuan ; Fa, Naiguang

Author_Institution

Sch. of Electr. Eng., Shenyang Univ. of Technol.

Volume

3

fYear

2006

fDate

14-16 Aug. 2006

Firstpage

1

Lastpage

5

Abstract

Sliding mode controller using a recurrent fuzzy neural network (RFNN) is presented, in which RFNN is utilized to estimate the real-time lumped uncertainty for the position control of permanent magnet linear synchronous motor (PMLSM) drive system, so that the control effort can be reduced. Considering the convergence rate, global feed-forward RFNN is employed instead of global feedback RFNN. Furthermore, space vector modulation (SVM) that can decrease the vector deviation is adopted. Simulation results show that the proposed new recurrent fuzzy neural network sliding mode position control scheme provides a fast and robust regulation for the mover position

Keywords

feedforward neural nets; fuzzy control; fuzzy neural nets; linear synchronous motors; machine vector control; modulation; permanent magnet motors; position control; recurrent neural nets; synchronous motor drives; variable structure systems; PMLSM drive system; SVM; feed-forward RFNN; permanent magnet linear synchronous motor; position control; recurrent fuzzy neural network; sliding mode controller scheme; space vector modulation; vector control; Control systems; Fuzzy control; Fuzzy neural networks; Machine vector control; Position control; Real time systems; Sliding mode control; Support vector machines; Synchronous motors; Uncertainty; PMLSM; SVM; chattering; recurrent fuzzy neural network (RFNN); sliding mode control; vector control;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Electronics and Motion Control Conference, 2006. IPEMC 2006. CES/IEEE 5th International

Conference_Location

Shanghai

Print_ISBN

1-4244-0448-7

Type

conf

DOI

10.1109/IPEMC.2006.4778327

Filename

4778327