A Novel Distributed Bipartite Consensus Control of Nonlinear Multiagent Systems via Prioritized Strategy Approach
Xinjun Wang, Ben Niu, Lun Zhai, Jie Kong, Xiaomei Wang
Abstract
In this brief, the adaptive bipartite consensus tracking problem is considered for nonlinear MASs over signed directed graphs with unknown nonlinear functions in high-order and nonstrict-feedback form. By proposing a prioritized strategy, a more relaxation condition that a signed digraph only containing a spanning tree is used in this brief, and all the agents in the system can be achieved a bipartition. Besides, it is worth pointing out that the higher-order form and the nonstrict-feedback structure are a part of the each follower model, which makes the controller design process more complicated. A distributed neural-based adaptive backstepping technology is applied, where the unknown nonlinear functions are approximated by Neural networks (NNs). Under the proposed protocol, all the agents achieve the bipartite consensus with bounded errors. The feasibility of the newly designed method are verified by MATLAB simulation analysis.