Distributed Cooperative Learning Control of Uncertain Multiagent Systems With Prescribed Performance and Preserved Connectivity

Abstract

For an uncertain multiagent system, distributed cooperative learning control exerting the learning capability of the control system in a cooperative way is one of the most important and challenging issues. This article aims to address this issue for an uncertain high-order nonlinear multiagent system with guaranteed transient performance and preserved initial connectivity under an undirected and static communication topology. The considered multiagent system has an identical structure and the uncertain agent dynamics are estimated by localized radial basis function (RBF) neural networks (NNs) in a cooperative way. The NN weight estimates are rigorously proven to converge to small neighborhoods of their common optimal values along the union of all agents' trajectories by a deterministic learning theory. Consequently, the associated uncertain dynamics can be locally accurately identified and can be stored and represented by constant RBF networks. Using the stored knowledge on identified system dynamics, an experience-based distributed controller is proposed to improve the control performance and reduce the computational burden. The theoretical results are demonstrated on an application to the formation control of a group of unmanned surface vehicles.