Model-Free Adaptive Control for a Class of MIMO Nonlinear Cyberphysical Systems Under False Data Injection Attacks

Abstract

In this article, a novel model-free adaptive control (MFAC) design for a class of multiple input multiple output (MIMO) nonlinear cyber-physical systems (CPSs) under false data injection (FDI) attacks in the measurement channel is proposed. First, a threshold attack detection scheme based on the difference between predicted and transmission data is proposed. Then, an attack compensation module is designed, which can be changed according to different attack strategies of the attacker, and can realize resilient control of FDI attacks on systems with different complexity and different attack intensities. Subsequently, a model-free adaptive controller based on partial form dynamic linearization is designed for the reconstructed system after attack compensation, which can indirectly realize the tracking control task of the original system. Theoretical analysis of the proposed algorithm shows that the tracking error is ultimately bounded. Finally, a numerical simulation and a three-tank system simulation are given to demonstrate the effectiveness of the proposed attack detection scheme, the flexibility of the resilient control of attack compensation, and the uniformly bounded convergence of tracking error.