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In this paper, we present adaptive tracking control designs for a class of constrained nonlinear systems with completely unknown constants. A Barrier Lyapunov Function (BLF) is adopted to protect this limit from being destroyed. By guaranteeing negative semi-definite of the BLF during the design process, we keep the constraint is not transgressed. Adaptive control is used to address the unknown constants...
This paper, based on radial basis function (RBF) neural network, presents an novel adaptive robust controller for a class of strict-feedback uncertainty nonlinear systems to address the tracking problem. The proposed approach, takes advantage of RBF neural network approximation property to approximate system uncertainties, and utilizes adaptive backstep-ping techniques for eliminating the effects...
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