Improved adaptive dynamic surface control for a class of strict-feedback nonlinear systems

Based on radial basis function neural networks (RBFNNs), adaptive dynamic surface control (DSC) is investigated for a class of uncertain strict-feedback nonlinear systems in this paper. By introducing first-order filter and combining DSC with backstepping, the operation of differentiation is replaced by simpler algebraic operation. Furthermore, the explosion of complexity in traditional backstepping design is avoided. It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness of all signals in the closed-loop system, with arbitrary small tracking error by appropriately choosing design constants.