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)
In this paper, the problem of designing distributed LQR for multi-agent systems is considered when the states are not available for the control purpose. Two types of distributed observer-based LQR controller are developed with local observers and distributed observers. The design procedure provides a systematic way to construct control and observer gains(More)
In this paper, a Robust adaptive neural network controller (RANNC) based on variable structure system for robotic manipulators is proposed to alleviate the problems met in practical implementation using classical variable structure controllers. The chattering phenomenon is eliminated by substituting single-input single-output radial-basis-function neural(More)