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Based on a novel radial basis function neural networks (RBF NNs), the nonlinearities are tackled without the prior knowledge of NN nodes and weights. The approximate coordinate coordination is combined with an auxiliary system to attenuate the effects generated by input saturation. Then, an opportune backstepping design procedure is presented using the barrier Lyapunov function (BLF) and RBF NN. Based on this design procedure, an adaptive state\u2013feedback controller is constructed, which makes the closed-loop system semi-globally uniformly ultimately bounded, the tracking error bounded in a compact set of the origin, and the full-states not violated. 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