Gaussian radial basis function neural network controller of a synchronous reluctance motor in electric motorcycle applications

In this article, a sliding mode control (SMC) design based on a Gaussian radial basis function neural network (GRBFNN) is proposed for the synchronous reluctance motor (SynRM) system in electrical motorcycle applications. The conventional SMC assumes that the upper lumped boundaries of parameter variations and external disturbances are known, and the sign function is used. This causes high-frequency chattering and the high-gain phenomenon. In order to avoid these drawbacks, the proposed method utilizes the Lyapunov stability method and the steep descent rule to guarantee the convergence asymptotically, and reduce the magnitude of the chattering or avoid it completely. Finally, numerical simulations are shown to illustrate the good performance of our controller design.

Authors and Affiliations

1. Electronic Vehicle and System Verification Group R & D Division, Automotive Research and Testing Center, Changhua, Taiwan, ROC

   Chien-An Chen

2. Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin, 640, Taiwan, ROC

   Huann-Keng Chiang

3. Department of Optoelectronic Engineering, Far East University, Tainan, Taiwan, ROC

   Wen-Bin Lin

Corresponding author

Correspondence to Huann-Keng Chiang.

This work was presented in part at the 15th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2010