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In this study a model reference rough-radial basis function neural network controller with feedback error learning for control of a class of nonlinear systems subject to unknown bounded uncertainty is proposed. The proposed controller in hybrid form includes the classic controller and rough- radial basis function neural network controller. Because of using the classic controller with the neural network controller, it is expected that the transient response is bounded. The weights of the output layer of the neural network controller are interval variables. Using an appropriate Lyapunov function, stable adaptation laws for these weights according to the output of the classic controller and based on stability are derived. To show the efficacy of the proposed controller, results of simulation that is applied to Duffing Oscillator and Genesio- Tesi are shown and results are compared with the results when simple model reference radial basis function neural network is used as the controller. The results show that the proposed method is more robust against uncertainty when it is compared to model reference radial basis function neural network controller. Also, using the proposed controller, synchronization of chaotic systems is performed. The results verified the effectiveness of the proposed controller.

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This paper proposes an adaptive control method based on the feedback linearization technique and a proposed neural network,  for tracking and position control of an industrial manipulator. At first, it is assumed that the dynamics of the system are known and the control signal is constructed  by the feedback linearization method. Then to eliminate the effects of the uncertainties and external disturbances, the parameters of the proposed neural network are learned based on the Lyapunov method such that the sliding condition to be satisfied. The performance of the proposed method is compared with the sliding mode technique in presence of external disturbance, delay and uncertainties. The simulation results verify that the proposed method is effective and can be used in many applications.