High-Performance Inverse Artificial Neural Network Controller for Asynchronous Motor Control

Résumé

Induction motor (IM) is considered one of the most important machines in industrial applications, which requires precise and effective control of its behavior in order to improve its performance. In this paper, three control strategies based on the development of inverse artificial neural networks (IANNs) were proposed in order to control the current (Ias), electromagnetic torque (Ce), and speed (Wr) of an asynchronous machine IM. These inverse artificial neural networks have been learned from conventional control system (PI controller and vector control) data using MATLAB software. Comparison between the responses of both the classical controller and the IANNs showed the ability and effectiveness of the latter in precisely controlling the three properties of the asynchronous motor, and it also achieved better dynamic motor behavior, speed without overtaking, and good load disturbance rejection, which proves the high performance of these developed IANNs.

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