Abstract
Combining dynamic surface control with backstepping, a robust adaptive neural network control is proposed for a class of nonlinear systems in pure-feedback form with unmodeled dynamics and unknown dead-zones. The restriction of the control gain is relaxed by utilizing integral-type Lyapunov function. Using the radial basis function (RBF) neural networks (NNs) to approximate the unknown continuous functions, and with the help of Young's inequality, only one learning parameter needs to be tuned online in the whole controller design. The burdensome computation is alleviated. By theoretical analysis, the closed-loop control system is shown to be semi-globally uniformly ultimately bounded. Simulation results verify the effectiveness of the proposed approach.
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