Adaptive NN Distributed Control for Time-Varying Networks of Nonlinear Agents With Antagonistic Interactions
Qingling Wang, Haris E. Psillakis, Changyin Sun, Frank L. Lewis
Abstract
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 δ -connected. Finally, simulation examples are given to illustrate the effectiveness of the NN distributed control algorithms.