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Robust Compensation with Adaptive Fuzzy Hermite Neural Networks in Synchronous Reluctance Motors

Chao-Ting Chu¹ and Hao-Shang Ma²

1. Chunghwa Telecom Laboratories, Internet of Things Laboratory
   No.99, Dianyan Rd., Yangmei District, Taoyuan City 32661, Taiwan, R. O. C.
   chaot@cht.com.tw
2. Department of Computer Science and Information Engineering, National Taichung University of Science and Technology
   No. 129, Section 3, Sanmin Road, North District, Taichung City 404336, Taiwan, R. O. C.
   hsma@nutc.edu.tw

Abstract

In this paper, a robust compensation scheme using adaptive fuzzy Hermite neural networks (RCAFHNN), for use in synchronous reluctance motors (SRMs), is proposed. SRMs have a simple underlying mathematical model and mechanical structure, but are affected by problems related to parameter variations, external interference, and nonlinear dynamics. In many fields, precise control of motors is required. Although the use of neural network and fuzzy are widespread, such controllers are affected by unbound nonlinear system model. In this study, RCAFHNN, based on an adaptive neural fuzzy interface system (ANFIS), was used to bound motor system model controller algorithm. RCAFHNN can be characterized in three parts. First, RCAFHNN offers fuzzy expert knowledge, a neural network for online estimation, and recursive weight estimation. Second, the replacement of the Gaussian function by the Hermite polynomial in RCAFHNN enables reduced membership function training times. Third, the system convergence and robustness compensation of RCAFHNN were confirmed using Lyapunov stability. RCAFHNN ameliorates the problems of external load and system lump uncertainty. The experimental results, in which the output responses of RCAFHNN and ANFIS (adaptive neural fuzzy interface systems) were compared, demonstrated that RCAFHNN exhibited superior performance.

Key words

Synchronous reluctance motors, Lyapunov stability, Robust, Adaptive control, Neural network estimator, Adaptive laws

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS230803076C

Publication information

Volume 21, Issue 2 (April 2024)
Special Issue on Deep Learning Techniques in Intelligent Internet of Things and 5G Communication Networks
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Chu, C., Ma, H.: Robust Compensation with Adaptive Fuzzy Hermite Neural Networks in Synchronous Reluctance Motors. Computer Science and Information Systems, Vol. 21, No. 2, 569–592. (2024), https://doi.org/10.2298/CSIS230803076C