Adaptive neural control for switched nonlinear systems with unmodeled dynamics and unknown output hysteresis

Abstract This paper aims at addressing the adaptive neural control problem for switched nonlinear systems with output hysteresis and unmodeled dynamics. The switching law in this study is arbitrary. In our model, the unmodeled dynamics are assumed to be of input-to-state practical stability (ISpS). With the help of this assumption, a dynamic normalizing signal is constructed to dominate the unmodeled dynamics. And then, a direct adaptive neural state-feedback control scheme is developed with the help of approximation-based backstepping. The stability analysis shows that the system output is convergent to an adjustable small region of zero asymptotically, and furthermore, all the closed-loop signals are bounded. Finally, we further present two simulation examples to verify the effectiveness of our control scheme.

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