African Buffalo Optimized Generative Mamdani Fuzzy Controller based Deep Belief Network for Efficient Speed Control in PMSM

Abstract

Permanent Magnet Synchronous Motors (PMSM) are employed for highly efficient motor drive. PMSM are efficient, brushless, fast, safe, and have high dynamic performance. Many researchers pursued their areas of interest in PMSM in order to improve their performance through speed control. However, the PMSM’s efficiency was not reduced, and speed control was not carried out in an efficient manner. This problem is addressed by the African Buffalo Optimized Generative Mamdani Fuzzy Controller-based Deep Belief Network (ABOGMFC-DBN) model. Specifically, the ABOGMFC-DBN mode l is to handle the PMSM speed in order to attain a higher current value. The ABOGMFC-DBN model performs two processes: the multivariate African Buffalo hidden neuron and its weight optimization process, and the generative Mamdani fuzzy controller-based deep belief network process. The first procedure optimises the amount of hidden neurons in the deep belief network and its weight parameters. The PMSM speed is handled by the Mamdani fuzzy controller in the second step, which uses four layers. The mean square error (MSE) is then calculated in order to get the minimal rated current value using a Gaussian activation function. Finally, the PMSM’s performance improves. Using the PMSM parameter, the performance of the ABOGMFC-DBN model is evaluated on the basis of rising time, settling time, peak value, peak time, and peak overshoot. With a higher output current value compared to traditional techniques, the simulation findings of the ABOGMFC-DBN model enhance the PMSM’s performance.