Adaptive NN Cooperative Control of Unknown Nonlinear Multiagent Systems With Communication Delays

In this article, we address the distributed adaptive neural network (NN) control problem for approximate state consensus under communication delays. High-order agent models are considered with unknown nonlinearities and unknown, nonidentical control directions. A novel set of variables called proportional and delayed integral (PdI) consensus error variables are introduced that allow us to recast the approximate consensus problem as an approximate regulation problem. Each PdI variable associated with a certain agent uses only delayed measurements of its neighbors' states in accordance to our delayed communication protocol. Radial basis function (RBF) NNs are employed to approximate the unknown nonlinearities and distributed adaptive NN control laws with Nussbaum gains are proposed that ensure approximate consensus by steering all PdI variables to a neighborhood of zero. Simulation results are also presented that verify the validity of our theoretical analysis.

Automated summary

This article addresses the distributed adaptive neural network control problem for approximate state consensus under communication delays. It introduces novel variables called PdI consensus error variables to recast the problem as an approximate regulation one. The approach employs Radial Basis Function NNs for approximating unknown nonlinearities and proposes distributed adaptive NN control laws with Nussbaum gains to ensure approximate consensus.