Performance Improvement for BLDC Motor by using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

Abstract

In this project ANFIS based control of BLDC motor is presented.  Brushless DC motors (BLDC) find wide applications in industries due to their high power density and ease of control.To achieve desired level of performance the motor requires suitable speed controllers. The mathematical model of BLDC motor and a back propagation Adaptive Neuro-Fuzzy Inference Systems (ANFIS) algorithm are considered and included to replace the conventional method of Proportional Integral and Fuzzy. ANFIS it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework. Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to approximate nonlinear functions. Hence, ANFIS is considered to be a universal estimator. The analysis of overshoot, rise time and steady state error for the speed range which indicates that the proposed adaptive neuro-fuzzy inference systems has successfully improved the performance of the BLDC motor drive. According to new proposed approach speed control of BLDC motor drive and analysis using adaptive Neuro-Fuzzy inference systems to carry off the weakness of fuzzy logic controller (Steady-state error).Further the ANFIS controller provides low torque ripples and high starting torque. The proposed ANFIS controller is evaluated by using MATLAB/SIMULINK software.