A neuro-fuzzy multilayer weights approach for an on-line learning speed controller applied to a switched reluctance machine: why and how to use it

Author

Rafael, Silviano ; Pires, A.J. ; Branco, P. J Costa

Author_Institution

LabSEI, Instituto Politecnico de Setubal, Lisbon

fYear

2005

fDate

11-14 Sept. 2005

Abstract

The most used neuro-fuzzy motor speed control systems are time consuming and have an high computation effort when the speed reference changes suddenly and the system, most of the time, has to learn this new operating point. In this case a degradation of the system performance is evident, as is demonstrated by our experimental results in this paper. To surpass these effects, a new neuro-fuzzy multilayer control´s approach is proposed. The multilayer controller is tested and compared in the speed control system for an 8/6 switched reluctance motor by experimental tests. The proposed solution is explained, tested and the experimental results are presented and discussed

Keywords

angular velocity control; control engineering computing; electric machine analysis computing; fuzzy neural nets; machine control; neurocontrollers; reluctance motors; multilayer controller; neurofuzzy motor speed control systems; neurofuzzy multilayer weights approach; online learning speed controller; switched reluctance machine; Control systems; Electric variables control; Input variables; Machine learning; Nonhomogeneous media; Reluctance machines; Reluctance motors; System testing; Variable speed drives; Velocity control; Adjustable speed drive; Control methods for electrical systems; Neural control; Switched reluctance drive; Variable speed drive;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Electronics and Applications, 2005 European Conference on

Conference_Location

Dresden

Print_ISBN

90-75815-09-3

Type

conf

DOI

10.1109/EPE.2005.219488

Filename

1665678