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In this paper, a quantum intelligent robust controller via a combination of sliding mode control with boundary layer and quantum neural networks, for uncertain nonlinear systems in presence of external disturbances, is presented. Based on the adjustable time variant rejection regulator and rejection parameter, a time variant sliding surface as an adaptive chain of the first ordered low pass filters is defined. A three layers quantum neural network is designed to identify the uncertain nonlinear functions in system dynamics. In this method, the control gain and the break-frequency bandwidth are tuned adaptively. Also, the effects of uncertainties and the un-modeled frequencies are eliminated and chattering phenomenon doesn’t occur. Also, for facilitating analytical theory of the presented method and derivation of the adaptive laws a theorem is proved. Finally, the simulated examples show that the proposed method presents an intelligent adaptive robust tracking control such that the control amplitudes and the integral absolute error index of the tracking trajectory are much less than the other methods. Therefore, effective identification, eliminating the effects of system uncertainties, adjustable control gain and break-frequency bandwidth and more accurate tracking are some of the advantages of this method.
Keywords

Keywords: Sliding mode control, Boundary Layer, Quantum neural network, Break-frequency bandwidth, Rejection Regulator

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