Abstract
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.
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