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A robust adaptive tracking control approach is presented for a class of strict-feedback single-input-single-output nonlinear systems. By employing radial-basis-function neural networks to account for system uncertainties, the proposed scheme is developed by combining ??dynamic surface control?? and ??minimal learning parameter?? techniques. The key features of the algorithm are that, first, the problem...
In this paper, by incorporating the dynamic surface control technique into a neural network based adaptive control design framework, we have developed a backstepping based control design for a class of nonlinear systems in pure-feedback form with arbitrary uncertainty. Our development is able to eliminate the problem of ??explosion of complexity?? inherent in the existing method. In addition, the...
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