Intelligent Sliding-Mode Position Control Using Recurrent Wavelet Fuzzy Neural Network for Electrical Power Steering System

Faa Jeng Lin, Shih Gang Chen, I. Fan Sun

研究成果: 雜誌貢獻期刊論文同行評審

20 引文 斯高帕斯(Scopus)

摘要

A digital signal processor (DSP)-based intelligent sliding-mode control (SMC) is proposed for the position control of a six-phase permanent magnet synchronous motor (PMSM) drive system installed in an electric power steering (EPS) system in this study. First, the dynamic mathematical model of the EPS system is derived by the Lagrangian dynamics. Since the EPS system is a nonlinear and time-varying system, the control accuracy is very sensitive to the parameter variations and external disturbances. Therefore, a SMC is developed for the position control of the EPS system. However, the upper bound of the uncertainties is difficult to obtain in advance and the choice of switching control gain in SMC is vital but time-consuming and may cause undesired chattering phenomenon. Hence, an intelligent SMC with a novel recurrent wavelet fuzzy neural network (ISMC-RWFNN) is proposed, in which a recurrent wavelet fuzzy neural network (RWFNN) is adopted as an uncertainty estimator to overcome the aforementioned disadvantage of SMC. Moreover, a robust compensator is employed to reduce the estimation error. In addition, the adaptive learning algorithms for the online training of the RWFNN are derived using the Lyapunov theorem and Taylor series. Finally, the proposed ISMC-RWFNN to control the position of a six-phase PMSM drive system for the EPS system is implemented in a 32-bit floating-point DSP, and some experimental results are provided to verify its effectiveness.

原文???core.languages.en_GB???
頁(從 - 到)1344-1361
頁數18
期刊International Journal of Fuzzy Systems
19
發行號5
DOIs
出版狀態已出版 - 1 10月 2017

指紋

深入研究「Intelligent Sliding-Mode Position Control Using Recurrent Wavelet Fuzzy Neural Network for Electrical Power Steering System」主題。共同形成了獨特的指紋。

引用此