Resilient Nash Equilibrium Seeking in Multiagent Games Under False Data Injection Attacks
针对遭受虚假数据注入攻击的双积分器多智能体非合作博弈，提出一种基于扩展状态观测器的弹性纳什均衡求解算法，无需攻击信息即可使系统收敛到均衡点。
This article proposes a resilient distributed Nash equilibrium (NE) seeking algorithm for noncooperative games with multiple double-integrator agents who suffer from false data injection (FDI) attacks. A malicious attacker injects false data into agents’ actuators and sensors so that agents’ strategies deviate from the NE of the game with the compromised control inputs and interactive information. First, the robustness of the seeking algorithm against the FDI attacks is analyzed. Then, to mitigate the effect of the attacks on agents’ strategies, the false data injected in the actuators and sensors are regarded as extended states which can be observed by extended state observers (ESOs). Thus, a resilient NE seeking algorithm is proposed based on ESOs. The resilient algorithm can drive the system to converge to the NE without requiring any information about the nature of the attacks. An explicit criterion is given to ensure the effectiveness of the designed algorithms. An example is given to illustrate the results.