Compensation-Based Distributed Model-Free Adaptive Control for Cyber-Attacks
Zhenzhen Pan, Ronghu Chi, Zhongsheng Hou
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 cyber-attacks. The convergence is proved rigorously in the sense of mathematical expectation. The simulation study further confirms the effectiveness of the proposed cDMFAC method.