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In this study, an adaptive neural backstepping control scheme is proposed for a class of strict‐feedback non‐linear systems with unmodelled dynamics, dynamic disturbances and input saturation. To solve the difficulties from the unmodelled dynamics and input saturation, a dynamic signal and smooth function in non‐affine structure subject to the control input signal are introduced, respectively. Radial basis function (RBF) neural networks are used to approximate the packaged unknown non‐linearities, and an adaptive neural control approach is developed via backstepping, which guarantees that all the signals in the closed‐loop system are semi‐globally uniformly ultimately bounded in mean square. The main contributions of this note lie in that a control strategy is provided for a class of strict‐feedback non‐linear systems with unmodelled dynamics uncertainties and input saturation, and the proposed control scheme does not require any information of the bound of input saturation non‐linearity. Simulation results are used to show the effectiveness of the proposed control scheme.
Wang et al. (Tue,) studied this question.