Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive

Author

Lin, Faa-Jeng ; Lin, Chih-Hong ; Shen, Po-Hung

Author_Institution

Dept. of Electr. Eng., Nat. Dong Hwa Univ., Hualien, Taiwan

Volume

9

Issue

5

fYear

2001

fDate

10/1/2001 12:00:00 AM

Firstpage

751

Lastpage

759

Abstract

A self-constructing fuzzy neural network (SCFNN) which is suitable for practical implementation is proposed. The structure and the parameter learning phases are performed concurrently and online in the SCFNN. The structure learning is based on the partition of input space and the parameter learning is based on the supervised gradient decent method using a delta adaptation law. Several simulation and experimental results are provided to demonstrate the effectiveness of the proposed SCFNN control stratagem with the implementation of a permanent-magnet synchronous motor speed drive. Moreover, the simulation results of time varying and nonlinear disturbances are given to show the dynamic characteristics of the proposed controller over a broad range of operating conditions

Keywords

angular velocity control; fuzzy neural nets; gradient methods; learning (artificial intelligence); neurocontrollers; permanent magnet motors; self-organising feature maps; synchronous motor drives; gradient decent method; parameter learning; permanent-magnet motor; self-constructing fuzzy neural network; speed control; structure learning; synchronous motor drive; Control nonlinearities; Control systems; Error correction; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Neural networks; Nonlinear control systems; Partitioning algorithms; Synchronous motors;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/91.963761

Filename

963761