An Improved Self-tuning Control Mechanism for BLDC Motor Using Grey Wolf Optimization Algorithm

Abstract

Brushless DC motor employed wide role actuator plays a significant role in many real-time applications. This paper investigates modelling and simulation of BLDC motor with an optimization algorithm for self-tuning parameters in an unknown alleyway. Grey wolf algorithm (GWA), an intelligent control algorithm is developed with the behaviour of a wolf while hunting the pathways. Further the algorithm also reduces noise cancellation that accurately reduces the impact on load during unknown alleyways. GWA is employed to acquire the gain values and self-tune parameters for the inverter to drive BLDC motor, and constant term is introduced to reduce overreach of motor speed and position of shaft. A comparative analysis is conducted among the feedback controller for BLDC motor optimization such as neuro-ANN and fuzzy-PID through simulation. Results suggest the proposed GWA algorithm holds better performance and reduce error in comparison with the other two optimization methods.