Adaptive finite-time stabilization of a class of switched nonlinear systems using neural networks

This paper investigates the adaptive finite-time stabilization of a class of switched nonlinear systems via backstepping technique, where unknown nonlinear functions are only required to be continuous. At each step of backstepping, neural network is used to appropriate the unknown continuous function, an adaptive law and a virtual controller are constructed simultaneously. By using the Lyapunov function method, it is shown that, under the designed controller and adaptive laws, the state of the closed-loop system is semi-globally uniformly finite-time bounded. An example is provided to show the effectiveness of the proposed method.

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