Observer-Based Distributed Model-Free Adaptive Iterative Learning Containment Control for Unknown Nonlinear MASs with Dynamic Event-Triggered Strategy

This article investigates the issue of distributed model-free adaptive iterative learning containment control for unknown nonlinear multi-agent systems (MASs) with dynamic event-triggered strategy. Firstly, the containment error is converted into compact form dynamic linearization by using the dynamic linearization method. Moreover, a dynamic event-triggered strategy is provided for distributed controller to deal with limited bandwidth problem. On this basis, a novel adaptive containment control approach for problem investigated is set up by iterative learning method and event-triggered strategy. The designed controller only uses the I/O signals, the controlled model information is not required. Distributed controllers of each agent are constructed to guarantee that the outputs of followers converge to a convex hull formed by the outputs of leaders in finite time. Finally, a digital simulation is also introduced to depict the reliability of the introduced control protocol.