Bipartite containment tracking for nonlinear MASs under FDI attack based on model-free adaptive iterative learning control

The bipartite containment control problem for a type of heterogeneous multi-agent systems (MASs) under false data injection (FDI) attack is handled in this work by using the distributed model-free adaptive iterative learning control scheme with attack compensation. The unknown non-affine nonlinear dynamics of each agent is first transformed into an equivalent attack-related data model along the iteration axis using a compact form dynamic linearization method. Then, a distributed model-free adaptive iterative learning bipartite containment control (DMFAILBCC) scheme is constructed by employing I/O data from MASs, and the convergence is proved by rigorous mathematical analysis In addition, the updated control method and the convergence analysis will be extended to iteration switching topologies. Finally, the performance of the two proposed schemes is validated through numerical simulations and comparisons with different control schemes.