Adaptive Speed Controller for a Permanent Magnet Synchronous Motor

Omar Aguilar Mejía, Rubén Tapia Olvera, Iván de Jesús Rivas Cambero, Hertwin Minor Popocalt


This paper presents a controller performance that is develop employing an adaptive B-spline neural network algorithm for adjusting the rotor speed of the permanent magnet synchronous motor. It includes a comparative analysis with three control strategies: conventional proportional integral, sliding mode and fuzzy logic. Also, gives a systematic way to determine the optimal control gains and improve the tracking error performance. A methodology for the adaptive controller and its training procedure are explained. The efficacy of the proposed method is analyzed using time simulations where the motor is subjected to disturbances and reference changes. The proposed control technique exhibits the best performance because it can adapt to every condition, demanding low computational effort for an on-line operation and considering the system nonlinearities.


fuzzy logic; artificial neural network; motor drives; sliding mode; permanent magnet machines

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