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

• Main Authors: Liu Sheng, Guo Xiaojie, Zhang Lanyong
• Format: Article
• Language: English
• Published: IEEE 2017-01-01
• Series: IEEE Access
• Subjects: Permanent magnet synchronous motor (PMSM); adaptive backstepping sliding mode (ABSM); nonlinear robust control; recurrent wavelet fuzzy neural network (RWFNN); uncertainty observer
• Online Access: https://ieeexplore.ieee.org/document/7962286/

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.