Artificial neural network controller for improved performance of brushless DC motor

This paper presents the development and performance analysis of model reference adaptive controller using Artificial Neural Network (ANN) for Brushless DC motor (BLDC) drives. The model reference adaptive systems (MRAS) have a parameter adjustment mechanism along with the normal feedback loop and hence give better solutions when there are variations in process parameters. Neural networks (NNs) with their inherent parallelism, learning capabilities and fault tolerance have proven to be a promising solution in estimating and controlling nonlinear systems. This paper combines a MRAS with ANN to solve the problems of non-linearity, parameter variations and load excursions that occur in BLDC motor drive systems. The performance of the traditional PID controller based speed control method is compared with the model reference based speed control for BLDC motor drive system using MATLAB Simulink software. Experimental results using TMS320LF2407A is presented to prove that the MRAC based model is capable of speed tracking as well as reduce the effect of parameter variations.