Robust adaptive control with unmodeled dynamics and unknown dead-zones

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.