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This paper presents a novel robust adaptive trajectory linearization control (RATLC) method for a class of uncertain nonlinear systems using radial basis function neural networks (RBFNN). TLC is a promising nonlinear tracking and decoupling control method, which has experienced growing interests and popularity recently. However it may exhibit poor performance when uncertainties exist and turn large. Radial basis function neural networks are introduced to approximate the uncertainties online from available measurements. A robust adaptive signal is added to compensate for the estimation error of the neural network output. Conditions are derived which guarantee ultimate boundedness of all the errors in the combined system. Excellent disturbance attenuation ability and strong robustness of the proposed RATLC method are demonstrated by an numerical example.