Abstract

This study proposes a soft computing speed controller where recurrent neural network speed controller is trained by genetic extended kalman filter, based on a predetermined genetic Proportional Integral (PI) controller. A genetic PI gives to this proposed speed controller exact values without mathematical equations, unlike the classical PI who is need for it necessary. With this proposed method the results of simulation obtained is as they are of real state of the Permanent Magnet Synchronous Motor (PMSM). The harmonic ripples of speed response are also minimized with Genetic Extended Kalman Filter (GEKF) and Neural Network Space Vector Modulation (NNSVM). This fusion between the Artificial Intelligence (AI) and optimized method of the extended kalman filter gives more superiority to the method proposed, like shows the simulations results compared to classical Neural Network Controller (NNC).

Keywords:

Direct torque control, extended kalman filter, genetic algorithm, permanent magnet synchronous motor, recurrent neural network, space vector modulation,

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Competing interests

The authors have no competing interests.

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