Adaptive NN Consensus Control for Second-Order Nonlinear Multi-Agent Systems Against Sparse Sensor Attacks

Xiao Tang, Yang Yu, Wei Wang

Abstract: This paper investigates the secure consensus tracking control problem of second-order nonlinear multi-agent systems against sparse sensor attacks. A secure data selector is designed to extract unattacked output data from a set of output measurements affected by sensor attacks. Subsequently, utilizing the unattacked output data, a neural network (NN) secure state observer is constructed to estimate the unavailable system states. Then, an adaptive NN consensus controller is proposed via dynamic surface control technique. The designed control method ensures that all signals of the closed-loop systems are ultimately bounded, and consensus tracking control is achieved with bounded tracking errors in the presence of sparse sensor attacks. Finally, the effectiveness of the proposed control scheme is validated through a simulation of unmanned aerial vehicle attitude control systems.

Submission Number: 30