Intelligent Backstepping Control of Synchronous Reluctance Motor Drive System

Faa Jeng Lin, Shih Gang Chen, Che Wei Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

An intelligent backstepping control (BSC) using recurrent feature selection fuzzy neural network (RFSFNN) is proposed to construct a high-performance synchronous reluctance motor (SRM) position drive system. First, the dynamics of the SRM position drive system and the BSC are briefly introduced. However, the lumped uncertainty of the SRM is unavailable to obtain in advance. Therefore, an intelligent backstepping control using recurrent feature selection fuzzy neural network (IBSCRFSFNN), which combines the advantages of recurrent neural network, fuzzy logic system and feature selection method, is developed to approximate an idea BSC and to maintain the stability of SRM position drive system. The network structure and online learning algorithm of the IBSCRFSFNN are described in detail. At last, the proposed control system is implemented in a floating-point TMS320F28075 digital signal processor. The experimental results are illustrated to show the validity of the proposed intelligent BSC system.

Original languageEnglish
Title of host publication2018 International Automatic Control Conference, CACS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538662786
DOIs
StatePublished - 9 Jan 2019
Event2018 International Automatic Control Conference, CACS 2018 - Taoyuan, Taiwan
Duration: 4 Nov 20187 Nov 2018

Publication series

Name2018 International Automatic Control Conference, CACS 2018

Conference

Conference2018 International Automatic Control Conference, CACS 2018
Country/TerritoryTaiwan
CityTaoyuan
Period4/11/187/11/18

Fingerprint

Dive into the research topics of 'Intelligent Backstepping Control of Synchronous Reluctance Motor Drive System'. Together they form a unique fingerprint.

Cite this