摘要
This paper is concerned with the problem of adaptive bipartite tracking problem for multi-agentsystems (MASs) over signed directed graphs with unknown nonlinear functions. Each followingagent is modelled by a higher-order nonlinear system in nonstrict-feedback form. The virtualand the actual control items of the considered system are the power functions with positive oddintegers rather than linear items. A distributed neural-based adaptive backstepping scheme isproposed, where the unknown nonlinear dynamics are approximated by Neural networks. Underthe proposed protocol, the bipartite output tracking errors converge to a small neighbourhoodof the origin and all the signals of the closed-loop system remain semiglobally uniformly ultimatelybounded. Since the considered high-order non-strict feedback nonlinear MASs in ourpaper include some existing nonlinear MASs as the special case, our result can be extended tocontrol more general nonlinear MASs. Finally, the simulation results illustrate the validity of theproposed schemes by a numerical example and a practical example about a group of forceddamped pendulums.
基金
the National Natural Science Foundation of China(Grant Number 61873151)
in part by the Shandong Provincial Natural Science Foundation of China(Grant Number ZR2019MF009)
the Taishan Scholar Project of Shandong Province of China(Grant Number tsqn201909078).
