Adaptive Intelligent Resilient Bipartite Formation Control for Nonlinear Multiagent Systems With False Data Injection Attacks on Actuators and Sensors

An adaptive intelligent resilient distributed output bipartite time-varying formation protocol is proposed for a class of second-order uncertain nonlinear multiagent systems (MASs) with unknown attacks. Actuators and sensors are both vulnerable to unknown false data injection (FDI) attacks, and the proposed protocol does not require the removal of misbehaving agents or strong network connectivity restrictions. However, existing research methods are mainly limited to studying the complete cooperative relationship and attacks only on actuators or sensors. Network interactions are based on directed signed topologies, reflecting cooperation and competition between agents, and the corresponding adjacency matrix is no longer nonnegative, making traditional consensus controls strategy inapplicable and analyzed by gauge transformation matrix. Due to the uncertain nonlinear dynamics with unmeasurable states, unknown attacks would jeopardize the synchronization of bipartite formation control and even deteriorate entire systems. To address this issue, a security state estimator and adaptive intelligent state reconstruction technique are adopted. It not only can estimate and mitigate malicious unknown FDI attacks on both actuators and sensors simultaneously but also achieve uniform ultimate boundedness (UUB) for observer errors and prescribed time-varying bipartite group consistency formation performance. In particular, the proposed method overcomes the restriction that the dynamics must be linear or general Lipschitz-type nonlinear conditions. Finally, employing Riccati equation and linear matrix inequality, the theoretical method is validly proved by constructing proper Lyapunov through transformation matrix. The results of digital simulation can be effectively demonstrated.