Adaptive controller with RBF neural network for induction motor drive

In this paper, the radial basis function neural network-based model reference adaptive speed control for vector controlled induction motor drive system is presented. The speed control of induction motors is challenging because of their complex mathematical model, non-linear structure, and time varying dynamics. The radial basis function neural network is used to compensate the non-linearity which comes from the non-linear state equations of induction motor model. Neural network parameters are online updated via gradient descent algorithm to minimize the error. The drive system has been tested under various operating conditions. This paper demonstrated benefits of the proposed control approach by comparing the algorithm to conventional PI controllers. The results show that the proposed controller ensures good robustness and stable operation of the system under variable speed and variable loads than the PI controller.

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