Data-Driven Optimal Output Tracking Control of Heterogeneous Multiagent Systems Under DoS Attacks

This paper addresses the output tracking problem for heterogeneous multiagent systems (MASs) with unknown dynamics under denial-of-service (DoS) attacks. We propose a resilient distributed observer incorporating an attack compensator to estimate the leader’s state while mitigating the negative impact caused by network attacks. Additionally, we develop a data-driven learning algorithm circumventing the need for system dynamics knowledge. Compared with existing methods, the framework offers two key advantages: 1)the designed resilient distributed observer can dynamically reconstruct the system data lost during DoS attacks; 2)the developed learning algorithm eliminates the dependence of historical storage data. Finally, a numerical simulation is given to exemplify the effectiveness and superiority of the theoretical method.