Adaptive NN Distributed Control for Time-Varying Networks of Nonlinear Agents With Antagonistic Interactions

This article proposes an adaptive neural network (NN) distributed control algorithm for a group of high-order nonlinear agents with nonidentical unknown control directions (UCDs) under signed time-varying topologies. An important lemma on the convergence property is first established for agents with antagonistic time-varying interactions, and then by using Nussbaum-type functions, a new class of NN distributed control algorithms is proposed. If the signed time-varying topologies are cut-balanced and uniformly in time structurally balanced, then convergence is achieved for a group of nonlinear agents. Moreover, the proposed algorithms are adopted to achieve the bipartite consensus of high-order nonlinear agents with nonidentical UCDs under signed graphs, which are uniformly quasi-strongly delta-connected. Finally, simulation examples are given to illustrate the effectiveness of the NN distributed control algorithms.

Automated summary

An adaptive neural network (NN) distributed control algorithm is proposed for a group of high-order nonlinear agents with nonidentical unknown control directions (UCDs) under signed time-varying topologies. The algorithm achieves convergence for a group of nonlinear agents if the topologies are cut-balanced and uniformly structurally balanced in time. Simulation examples are provided to illustrate the effectiveness of the proposed algorithms in achieving bipartite consensus of high-order nonlinear agents on signed graphs.