Distributed consensus tracking for non‐linear multi‐agent systems with input saturation: a command filtered backstepping approach
Guozeng Cui, Shengyuan Xu, Frank L. Lewis, Baoyong Zhang, Qian Ma · IET Control Theory and Applications · 2016
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.