Design of BLDC Motor Using African Vulture Optimization Algorithm Based Trained FNN

Brush-less DC motors with permanent magnets are widely used in robotics, electric vehicles, and other industrial applications. Enhancing the performance of a BLDC motor can indeed be challenging due to the presence of non-linearities and complex design considerations. The present research aims to increase the effectiveness and performance of BLDC motors by using a feed-forward neural network (FNN). It is possible to quickly determine the FNN’s appropriate weight and bias settings by using the cutting-edge African Vulture Optimisation Algorithm (AVOA). Combining biological inspiration, machine learning, and motor technology, it develops a distinctive and promising way to improve the performance of BLDC motors. This study also includes the sensitive analysis of controlling parameters of the AVOA to investigate its effect on the statistical variables of the fitness function. This aids in optimizing the algorithm for better outcomes. The proposed method is then contrasted with various FNN learning algorithms based on Practical Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and PSO-GSA. Considerations like convergence rate and the capacity to steer clear of local minima are probably evaluated throughout the comparison. According to the results, AVOA-based optimization performs better than the other methods (PSO, GSA, and PSO-GSA) in terms of convergence speed and its ability to avoid becoming stuck in local minima. This suggests that the AVOA is useful for training FNNs to optimize the design parameters of BLDC motors.