Predefined-time control for nonlinear multi-agent systems with dual-terminal switching topologies: an iterative learning control approach

The iterative learning based predefined-time tracking consensus control issue is investigated for a class of nonlinear multi-agent systems with dual-terminal switching topologies in this paper. To relax the restrictions of effective spanning trees for communication relationship among agents, the dual-terminal switching topologies mechanism is established, whereby the information loss along the time axis is compensated by the iterative-varying topologies along the iteration axis. Based on the iterative learning control technique, the iterative tracking error is introduced to the calculating process of predefined convergence time. An efficient computation approach is proposed for calculation of the predefined-time control parameters. The boundedness and convergence of consensus errors are verified by using the contraction-mapping approach. Finally, the effectiveness of the propounded iterative learning based predefined-time control method is testified through a numerical example.