Finite-time adaptive neural control for nonlinear systems under state-dependent sensor attacks

This article proposes a finite-time control scheme for a class of uncertain nonlinear systems in the presence of sensor attacks. Specifically, based on the backstepping technology and the nonlinear function approximation capability of radial basis function neural networks, we develop an adaptive controller, guaranteeing the finite-time stability of the closed-loop system despite sensor attacks. In particular, the unknown time-varying state-feedback gains caused by sensor attacks are handled by Nussbaum functions. To decrease the design difficulty of the finite-time controller, a novel practical finite-time stability criterion is given. Finally, two simulation examples are provided, demonstrating the effectiveness of the proposed adaptive control scheme.