Compensation-Based Distributed Model-Free Adaptive Control for Cyber-Attacks

Abstract

In this work, three different types of cyber-attacks are considered together to develop a unified data-driven control method for a nonlinear networked multi-agent system bypassing any modeling processes. To this end, a distributed output is defined for every agent to show its relationship with the adjacent agents. Then, the nonlinear dynamics of the distributed output is transformed into a linear data model by using a dynamic linearization method, which is further used to predict the distributed output of the agent if the unconfined cyber-attack occurs. By introducing a stochastic variable, the compensated distributed output is reformulated to build a relationship between the actually measured one and the virtually predicted one. In the sequence, a compensation-based distribute model-free adaptive control (cDMFAC) is proposed to resist the unconfined cyberattacks. The convergence is proved rigorously in the sense of mathematical expectation. The simulation study further confirms the effectiveness of the proposed cDMFAC method.