Self-constructing recurrent fuzzy neural network for DSP-based permanent-magnet linear-synchronous-motor servodrive

F. J. Lin, S. L. Yang, P. H. Shen

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

A self-constructing recurrent fuzzy-neural-network (SCRFNN) control system is proposed to control the position of the mover of a field-oriented control permanent-magnet linear-synchronous-motor (PMLSM) servodrive system to track periodic reference trajectories. The proposed SCRFNN combines the merits of self-constructing fuzzy neural network (SCFNN) and the recurrent neural network (RNN). Moreover, the structure and the parameter-learning phases are preformed concurrently and on-line in the SCRFNN. 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. Further, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The simulated and experimental results due to periodic reference trajectories show that the dynamic behaviors of the proposed SCRFNN control system are robust with regard to uncertainties.

Original languageEnglish
Pages (from-to)236-246
Number of pages11
JournalIEE Proceedings: Electric Power Applications
Volume153
Issue number2
DOIs
StatePublished - Mar 2006

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