Synchronous reluctance motor speed drive using sliding mode controller based on Gaussian radial basis function neural network

被引：1
作者：
Chen C.-A. [1 ]
Chiang H.-K. [2 ]
Lin W.-B. [3 ]
Tseng C.-H. [4 ]
机构：
[1] Electronic Vehicle and System Verification Group, R and D Division, Automotive Research and Testing Center
[2] Department of Electrical Engineering, National Yunlin University of Science and Technology
[3] School of Engineering Science and Technology, National Yunlin University of Science and Technology
[4] Department of Electrical Engineering, Nan-Jeon Institute of Technology, Yen-Shui
来源：
Artificial Life and Robotics | 2009年 / 14卷 / 01期
关键词：
Lyapunov function; Radial basis function neural network; Sliding mode control; Synchronous reluctance motor;
D O I：
10.1007/s10015-009-0627-8
中图分类号：
学科分类号：
摘要：
In this article, a sliding mode control (SMC) design based on a Gaussian radial basis function neural network (GRBFNN) is proposed for a synchronous reluctance motor (SynRM) system robust stabilization and disturbance rejection. This method utilizes the Lyapunov function and the steep descent rule to guarantee the convergence of the SynRM drive system asymptotically. Finally, we employ experiments to validate the proposed method. © International Symposium on Artificial Life and Robotics (ISAROB). 2009.
引用
收藏
页码：53 / 57
页数：4
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