Observer-based adaptive neural network control design for nonlinear systems under cyber-attacks through sensor networks

This paper considers the adaptive neural tracking control for uncertain nonlinear strict-feedback systems under state-dependent cyber-attacks through sensor networks. As a distinctive feature, by constructing a novel adaptive neural observer, the estimates of system states are used for feedback control instead of the compromised system states corrupted by sensor attacks, such that the assumption on the derivative of attack weight can be removed from the adaptive controller design process. Then, an observer-based control scheme is developed such that all the system signals are bounded and the tracking error converges to a small neighborhood of zero. During the procedure of control design, adaptive backstepping technique is combined with radial basis function neural networks (RBFNNs) to construct controllers. Finally, two examples validate the efficacy of the proposed control scheme.