Robust Adaptive Backstepping Sliding Mode Control for Six-Phase Permanent Magnet Synchronous Motor Using Recurrent Wavelet Fuzzy Neural Network | Zendy

Liu Sheng | Zendy; Guo Xiaojie | Zendy; Zhang Lanyong | Zendy

Open Access

Robust Adaptive Backstepping Sliding Mode Control for Six-Phase Permanent Magnet Synchronous Motor Using Recurrent Wavelet Fuzzy Neural Network

Author(s) -

Liu Sheng,

Guo Xiaojie,

Zhang Lanyong

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2721459

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

A robust adaptive backstepping sliding mode control (ABSMC) with recurrent wavelet fuzzy neural network (RWFNN) is proposed for the speed regulation of a six-phase permanent magnet synchronous motor (PMSM) demonstrating parameter perturbations and load disturbances. First, a motor drive system model with lumped uncertainty is developed. Then, a nonlinear robust speed controller using ABSMC and H_∞ theory is presented. In this technique, ABSMC is employed to guarantee the speed tracking and parameter perturbation suppression; meanwhile, nonlinear H_∞ is utilized to minimize the influence of dynamic load disturbances on its tracking output. In addition, an uncertainty observer based on the RWFNN is designed to estimate the unknown and improve the robustness of motor drive system further. Ultimately, simulations and AppSIM simulator-based experimental results both indicate that the proposed control scheme can perfectly compensate the parameter perturbations and load disturbances while maintaining speed tracking precision.

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