Robust adaptive control for nonlinear cyber-physical systems with FDI attacks via attack estimation

This article investigates the adaptive control problem for nonlinear cyber-physical systems with network communication encountered false data injection (FDI) attacks. To address such attacks, the attack estimate method is designed whose objective is to minimize the vulnerability of FDI attacks. This article aim to find, using the historical FDI attack, a solution with guaranteed out-of-sample forecasting, so as for the attacker to plan its attacks such that the worst possible action on the system measurement. The approach is to formulate a robust optimization problem using the box-like sets, and then transform it into a linear programming model for solving problems. Consequently, under the framework of backstepping, a robust adaptive state-feedback control method is proposed. By using Lyapunov stability theory, the proposed control scheme can guarantee that all the closed-loop signals are globally bounded and the stabilization error converges to the origin. Finally, simulation results illustrate the effectiveness of the proposed control scheme.