Abstract
ABSTRACTA six-phase copper rotor induction motor (SCRIM) drive system with nonlinear uncertainties is controlled by a backstepping control system using a revamped recurrent Romanovski polynomials neural network (PNN). To achieve better control performance, an adaptive law of the revamped recurrent Romanovski PNN is based on the Lyapunov function to estimate the lumped uncertainty. Additionally, an error estimated law is proposed to compensate the estimated error. Meanwhile, the mended ant colony optimization (ACO) is used for regulating two variable learning rates in the revamped recurrent Romanovski PNN. Finally, some experimental results and a comparative analysis are presented to verify the effective of the proposed control system.
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