A HOIM-based iterative learning control scheme for multi-agent formation

In this paper, a high-order internal model (HOIM) based iterative learning control (ILC) scheme for multi-agent system (MAS) formation is studied. The HOIM-based ILC, which is an effective approach to deal with iteratively varying reference trajectories, provides a suitable framework for derivations and analysis of MAS control in general, and formation control in particular. In this work, the connections between agents are assumed dynamically changing at consecutive formation executions, which can be formulated as a series of structural switches. By employing the HOIM-based ILC, the control signals can be learned directly and tracking error converges asymptotically along the iteration axis.