Adaptive neural data-based compensation control of non-linear systems with dynamic uncertainties and input saturation


In this study, an adaptive neural backstepping management theme is proposed for a category 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 operate in non-affine structure subject to the management input signal are introduced, respectively. Radial basis operate (RBF) neural networks are used to approximate the packaged unknown non-linearities, and an adaptive neural management approach is developed via backstepping, which guarantees that every one the signals in the closed-loop system are semi-globally uniformly ultimately bounded in mean sq.. 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 also the proposed control scheme will not require any info of the certain of input saturation non-linearity. Simulation results are used to indicate the effectiveness of the proposed management theme.

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