A neo-fuzzy-neuron with real time training applied to flux observer for an induction motor

Author

Landim, Regis Pinheiro ; Rodrigues, Bruno ; Silva, Selenio Rocha ; Caminhas, Walmir Matos

Author_Institution

Univ. Fed. de Minas Gerais, Belo Horizonte, Brazil

fYear

1998

fDate

9-11 Dec 1998

Firstpage

67

Lastpage

72

Abstract

Presents an alternative algorithm for induction machines rotor flux observation. The novel procedure is based on a neo-fuzzy-neuron (NFN) with real time training. The main characteristics of this novel observer are: quick and accurate convergence and adaptability to system dynamics, requiring only the stator current measurements. The fuzzy-neural network employed here does not require previous training. The NFN is described, as well as its application to a rotor flux observer of a three-phase induction machine. Network training and observer performance are assessed by simulations and experimental results

Keywords

convergence; electric current control; fuzzy neural nets; fuzzy set theory; induction motors; machine control; observers; adaptability; flux observer; induction motor; neo-fuzzy-neuron; network training; observer performance; real time training; stator current measurements; system dynamics; three-phase induction machine; Convergence; Current measurement; DC motors; Electromagnetic fields; Induction motors; Magnetic flux; Motor drives; Rotors; Stators; Torque control;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1998. Proceedings. Vth Brazilian Symposium on

Conference_Location

Belo Horizonte

Print_ISBN

0-8186-8629-4

Type

conf

DOI

10.1109/SBRN.1998.730996

Filename

730996