Design and performance analysis of adaptive neuro-fuzzy controller for speed control of permanent magnet synchronous motor drive

Abstract

This article has been focused on the design of the artificial neural network with fuzzy inference system (ANFIS) for the speed control of permanent magnet synchronous motor (PMSM). PMSM is widely used in industrial applications such as robotic manipulators and machine tools due to the high efficiency, high torque to weight ratio and smaller size. One of the efficient control strategies of PMSM is based on ANFIS. ANFIS is very popular technique to deal with uncertainties. System dynamics in such cases can be compared with combining the proportional–integral–derivative (PID) with the Sliding Mode Controller (SMC). Simulations have been performed in MATLAB to validate the performance of the proposed model, and comparisons are made with ANFIS, SMC–PID and PID controllers compared to other controllers reported in the benchmark of the proposed controller’s efficiency. The proposed adaptive neuro-fuzzy-dependent results indicate good transient efficiency. Robustness against the robustness of adaptive neuro-fuzzy-based PID and SMC–PID controllers is satisfactory in terms of easy settling time, zero peaks overflow and zero steady state error. The simulation results have been implemented in MATLAB 2019b, and experimental results are implemented in BD63030.