Adaptive Fuzzy Neural Network-Based Speed Controller for Surface-Mounted Permanent Magnet Synchronous Motor: Design and Analysis

This work investigates an adaptive fuzzy neural network controller (AFNNC) that can accurately follow the speed reference of a surface-mounted permanent magnet synchronous motor (SPMSM) with system uncertainties. The proposed controller is designed by integrating three terms: the first term is used to cut down nonlinear components, the second term is used to offset for the nonlinearities of the model, and the third term is used to stabilize the dynamics of the system. The quadratic Lyapunov function is nominated to analyze global stability under the responsibility of a cost function. The simulated results are performed on Matlab/Simulink to validate the proposed AFNNC strategy in terms of good control performance (i.e., smaller steady-state error and greater robustness).