Finite-time adaptive neural resilient DSC for fractional-order nonlinear large-scale systems against sensor-actuator faults

The aim of this paper is to study an adaptive neural finite-time resilient dynamic surface control (DSC) strategy for a category of nonlinear fractional-order large-scale systems (FOLSSs). First, a novelty fractional-order Nussbaum function and a coordinate transformation method are formulated to overcome the compound unknown control coefficients induced by the unknown severe faults and false data injection attacks. Then, an enhanced fractional-order DSC technology is employed, which can tactfully surmount the deficiency of explosive calculations exposed in the backstepping framework. Furthermore, the radial basis function neural network is applied to address the unknown items related to the nonlinear FOLSSs. Based on the fractional Lyapunov stability criterion, a decentralized finite-time control approach is developed, which can ensure that all states of the closed-loop system are bounded and that the stabilization errors of each subsystem tend toward a small area in finite time. At last, two simulation examples are given to confirm the put-forward control algorithm's effectiveness.