Adaptive Fixed-Time Neural Control for Uncertain Nonlinear Multiagent Systems.

Abstract

In this article, we consider the problem of adaptive fixed-time tracking control for a class of multiagent systems (MASs) with mismatched uncertainty. Unlike the existing methodologies that only implement the practical finite-/fixed-time stability for MASs, a newly adaptive consensus control criterion is developed to reach fixed-time stability, where the controller design includes a series of newly Lyavonov functions and modified tuning functions. Radial basis function neural networks are employed to deal with the unknown functions in each agent, and the direct adaptive strategy solves the obstacle of "explosion of complexity." Under the performance-oriented controller, the error of the MASs converges to a predetermined interval within a fixed time. Two simulations illustrate the results obtained.