Hybrid Sensorless Speed Control Technique for BLDC Motor Using ANFIS Automation

Abstract

The Brushless Direct Current (BLDC) motors have shown to be a cost-effective alternative to traditional motors. The smooth and efficient operation of the BLDC motor is dependent on speed regulation. This research proposes a sensorless intelligent speed control technique for BLDC using an Adaptive Network-based Fuzzy Inference Systems (ANFIS) based Artificial Bee Colony (ABC) algorithm. The motor’s back EMF is measured, and ANFIS is used to generate Hall signals. The ABC is then utilized to provide the pulses needed for the three-phase inverter, avoiding the requirement of logic gate circuits. The input DC voltage to the inverter is controlled by a PI controller. The Optimized Field Oriented Control (OFOC) is implemented to control the sensorless BLDC motor. The proposed method is implemented and the outcomes are analyzed by MATLAB/SIMULINK and there is no overshoot and have low settling time and also the steady state error is very low than the existing methods. This proposed method can be improved by reducing the number of ANFIS controllers by incorporating a single controller whose main parameters shall be optimized by latest optimization techniques, and the results reveal that the proposed strategy is effective in managing the motor’s speed.