An intelligent second-order sliding-mode control for an electric power steering system using a wavelet fuzzy neural network

NCUIR > College of Electrical Engineering & Computer Science > Department of Electrical Engineering > journal & Dissertation > Item 987654321/106588

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106588

Title:	An intelligent second-order sliding-mode control for an electric power steering system using a wavelet fuzzy neural network
Authors:	林法正;Lin, Faa-Jeng;Hung, Ying-Chih;Ruan, Kai-Chun
Contributors:	資訊電機學院電機工程學系
Keywords:	Artificial neural networks;Asymmetric membership function;Control systems;Digital signal processors;Dynamical systems;Dynamics;Electric power;electric power steering (EPS);EPS;Estimators;Fuzzy control;Fuzzy logic;Fuzzy neural networks;Motors;Networks;Neural networks;second-order sliding-mode control;six-phase permanent magnet synchronous motor (PMSM);Sliding mode control;Synchronous motors;Torque;Uncertainty;wavelet fuzzy neural network (WFNN);Windings
Date:	2014-12-01
Issue Date:	2026-04-23 13:30:22 (UTC+8)
Publisher:	Institute of Electrical and Electronics Engineers Inc.;New York: IEEE
Abstract:	摘要： An intelligent second-order sliding-mode control (I2OSMC) using a wavelet fuzzy neural network with an asymmetric membership function (WFNN-AMF) estimator is proposed in this study to control a six-phase permanent magnet synchronous motor (PMSM) for an electric power steering (EPS) system. First, the dynamics of the steer-by-wire (SBW) EPS system and six-phase PMSM drive system with a lumped uncertainty are described in detail. Then, to alleviate the chattering phenomena in a traditional sliding-mode control (SMC), a second-order sliding-mode control (2OSMC) is designed. Moreover, the I2OSMC is developed to improve the required control performance of the EPS system. In the I2OSMC, the WFNN-AMF estimator with accurate approximation capability is employed to estimate the lumped uncertainty. Furthermore, the adaptive learning algorithms for the online training of the WFNN-AMF are derived using the Lyapunov theorem to guarantee the asymptotical stability of the closed-loop system. In addition, a 32-bit floating-point digital signal processor (DSP), i.e., TMS320F28335, is adopted for the implementation of the proposed control approach. Finally, some experimental results are illustrated to demonstrate the validity of the proposed I2OSMC using the WFNN-AMF estimator for the EPS system. 其他題名： TFUZZ 出版者： New York: IEEE 出版日期： 2014-12 出處： IEEE transactions on fuzzy systems, 2014-12, Vol.22 (6), p.1598-1611 資源來源： IEEE Electronic Library (IEL) 版權： Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Dec 2014 識別號： ISSN: 1063-6706 識別號： EISSN: 1941-0034 識別號： DOI: 10.1109/TFUZZ.2014.2300168 識別號： CODEN: IEFSEV
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	19	View/Open

社群 sharing

Loading...