Adaptive Neuro Fuzzy Technique for Speed Control of Six-Step Brushless DC Motor

H Abdelfattah,Mohamed I Mosaad,Nagwa F Ibrahim

doi:10.52549/ijeei.v9i2.2614

Abstract

The brushless DC motors with permanent magnets (PM-BLDC) are widely used in a miscellaneous of industrial applications. In this paper, The adaptive neuro fuzzy inference system (ANFIS) controller for Six-Step Brushless DC Motor Drive is introduced. The brushless DC motor’s dynamic characteristics such as torque , current , speed, , and inverter component voltages are showed and analysed using MATLAB simulation. The propotional-integral (PI) and fuzzy system controllers are developed., based on designer’s test and error process and experts. The experimential and hardware resuts for the inverter- driver circuits are presented. The simulation results using MATLAB simulink are conducted to validate the proposed (ANFIS) controller’s robustness and high performance relative to other controllers .

Highlights

Brushless DC (BLDC) motors are synchronous electrically powered motors; powered by DC electrical current
There are many problems associated with using classical PI controllers if applied to control the BLDC motors
Three optimization techniques were presented for optimal tuning of PID controller parameters for BLDC motor speed control, namely particle swarm optimization (PSO) and the Bacterial foraging (BF) technique [11],[12]

Summary

INTRODUCTION

Brushless DC (BLDC) motors are synchronous electrically powered motors; powered by DC electrical current. There are many problems associated with using classical PI controllers if applied to control the BLDC motors These problems are represented in the non-linearity in the system and the variation in the operating conditions, such as load variation with its consequences on the motor speed. The non-linearity problem has been addressed using many optimization techniques for optimal tuning of the PI/PID controller parameters [4,5,6]. This study showed the BLDC motor speed profile only during constant and variable loads without offering any other profiles for the electromagnetic torque, stator current and voltages. Another survey of speed control of BLDC motor with ANFIS controller was presented.

BLDC MOTOR OPERATING PRINCIPLE AND MODEL

FUZZY LOGIC CONTROLLER

RESULTS AND DISCUSSIONS

CONCLUSION