Rasoul Ghadami

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In this paper, an adaptive neural network multiple models sliding mode controller for robotic manipulators is presented. The proposed approach remedies the previous problems met in practical implementation of classical sliding mode controllers. Adaptive single-input single-output (SISO) RBF neural networks are used to calculate each element of the control(More)
In this paper, an adaptive multi-model CMAC-based controller (AMCBC) in conjunction with a supervisory controller is developed for uncertain nonlinear MIMO systems. AMCBC is a kind of adaptive feedback linearizing controller where nonlinearity terms are approximated with multiple CMAC neural networks With the help of a supervisory controller, the resulting(More)