A novel ACI motor vector method based on T-S-FCMAC neural network predictive control algorithm.

Author

Haichuan, Lou ; Wenzhan, Dai ; Meizhen, Lei

Author_Institution

Dept. of Autom. Control, Zhejiang Sci-Tech Univ., Hangzhou

fYear

2008

fDate

20-23 June 2008

Firstpage

232

Lastpage

237

Abstract

In this paper, a novel predictive control algorithm based on T-S-FCMAC neural network is presented for three phase ACI motor control. On the basis of the principle of vector control, T-S-FCMAC neural network is adopted to build predictive model for motor speed and stator torque current, and predictive control algorithm is put forward to design the regulator with golden selection for motor speed. The presented algorithm reduces the error between flux calculation and decouple part so that it improves greatly the performance of system. The simulation shows its effective.

Keywords

control engineering computing; electric machine analysis computing; electric motors; fuzzy control; machine vector control; neural nets; predictive control; ACI motor vector method; Terms-Takagi-Sugeno model; fuzzy cerebellar model articulation controller; motor speed; neural network predictive control algorithm; stator torque current; Machine vector control; Motor drives; Neural networks; Prediction algorithms; Predictive control; Predictive models; Rotors; Stators; Tellurium; Torque control; Fuzzy cerebellar model articulation controller (FCMAC); Golden selection method; Model predictive control; Takagi-Sugeno model; Vector control;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Automation, 2008. ICIA 2008. International Conference on

Conference_Location

Changsha

Print_ISBN

978-1-4244-2183-1

Electronic_ISBN

978-1-4244-2184-8

Type

conf

DOI

10.1109/ICINFA.2008.4608002

Filename

4608002