Secure Consensus for Multiagent Systems Under False Data Injection Attacks: A Hybrid Reinforcement Learning Scheme
This work addresses the problem of secure consensus in heterogeneous multiagent systems (MASs) under false data injection attacks (FDIAs). To balance the impact of malicious attacks against system performance, an $H_{\infty }$ consensus control scheme is developed, which treats attacks as worst case disturbances, atten...