Abstract
In this paper, a command filter and observer-based adaptive neural networks control scheme is put forward for the permanent magnet synchronous motors (PMSMs). The radial basis function (RBF) neural networks (NNs) approximation method are exploited to approximate the nonlinear terms of the PMSMs drive systems and an observer combined with RBF NNs is designed to estimate the rotor angular velocity of the PMSMs. Then, the command filtered control (CFC) technology is utilized to overcome the “explosion of complexity” problem inherent in the traditional backstepping control and decrease the filtering errors. Furthermore, the adaptive NNs backstepping method is employed to construct the controllers to guarantee the tracking error converge to a small neighborhood of the origin. Finally, simulation results illustrate the effectiveness of the proposed approach.
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