Distributed consensus tracking for non-linear multi-agent systems with input saturation: a command filtered backstepping approach

This study deals with the distributed consensus tracking problem for non-linear multi-agent systems under a fixed directed graph. The dynamics of the followers are taken as strict-feedback structures with unknown non-linearities and input saturation. Neural networks are utilised to identify a certain scalar related to the unknown non-linear functions, and an auxiliary system is introduced into the control design to compensate the effect of input saturation. By incorporating the command filtered technique into the backstepping design framework, a distributed consensus control scheme is constructed recursively. Using the Lyapunov stability theory, it is proved that all signals in the closed-loop systems are cooperatively semi-globally uniformly ultimately bounded and the consensus tracking errors converge to a small neighbourhood of origin by tuning the design parameters. Finally, simulation result demonstrates the effectiveness of the proposed control approach.