Intelligent backstepping control using recurrent feature selection fuzzy neural network for synchronous reluctance motor position servo drive system

Faa Jeng Lin, Shih Gang Chen, Che Wei Hsu

研究成果: 雜誌貢獻期刊論文同行評審

29 引文 斯高帕斯(Scopus)

摘要

The synchronous reluctance motor (SynRM) is a high-efficiency and low-cost motor with rugged structure. However, the nonlinear and time-varying control characteristics of the SynRM with high torque ripple confine the high-performance applications of this motor. Therefore, an intelligent backstepping control (BSC) using recurrent feature selection fuzzy neural network is proposed to construct a high-performance SynRM position servo drive system. First, the dynamic model of a vector control SynRM position servo drive is introduced. Second, a BSC system is designed for the tracking of the position reference. Since the lumped uncertainty of the SynRM position servo drive system is unavailable in advance, it is very difficult to design an effective BSC in practical applications. Moreover, the sign function in BSC will cause undesired chattering phenomenon, which will excite unknown dynamics and wear the ball bearing of the SynRM. To alleviate the existed difficulties in the BSC, the recurrent feature selection fuzzy neural network (RFSFNN) is proposed in this study to approximate an idea BSC. In addition, to compensate the possible approximated error of the RFSFNN, an improved adaptive compensator is augmented. The online learning algorithms of the RFSFNN are derived by using the Lyapunov stability method to assure asymptotical stability. Finally, the proposed control system is implemented in a 32-bit floating point digital signal processor. The effectiveness and robustness of the proposed intelligent BSC system are verified by some experimental results.

原文???core.languages.en_GB???
文章編號8417957
頁(從 - 到)413-427
頁數15
期刊IEEE Transactions on Fuzzy Systems
27
發行號3
DOIs
出版狀態已出版 - 3月 2019

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