Adaptive NN dynamic surface control of strict-feedback nonlinear systems

In this paper, a novel adaptive neural network (NN) dynamic surface control(DSC) is developed for a class of strict-feedback nonlinear systems with unknown virtual control gain functions. The explosion of complexity in traditional backstepping design is avoided by utilizing dynamic surface control and introducing integral-type Lyapunov function. Using Young´s inequality, only one parameter is adjusted at each recursive step in the backstepping design. It is shown 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. Simulation results verify the effectiveness of the approach.