Abstract
Abstract This paper addresses the problem of designing observer-based adaptive output-feedback tracking controls via neural networks for single-input/ single-output nonlinear systems which are unknown feedback linearizable continuous-time systems. A local convergence theorem is given on the tracking error and updating weight in the neural networks. Computer simulations verify the theoretical result.
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