Abstract
In this paper, adaptive prescribed performance output feedback dynamic surface control is proposed by introducing a performance function and an output error transformation for a class of nonlinear systems with unmodeled dynamics and unknown high-frequency gain sign. Radial basis function neural networks are used to approximate the unknown nonlinear functions. K-filters are employed to estimate the unmeasured states. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results illustrate the effectiveness of the proposed approach.
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