Model Reference Adaptive Consensus for Uncertain Multi-Agent Systems Under Directed Graphs

Abstract

The leaderless consensus problem of uncertain multi-agent systems is much challenging under general directed graphs due to the combination of uncertainties and the nonsymmetric Laplacian matrix. Motivated by the classical model reference adaptive control, in this paper, we propose a simple and efficient scheme, called the model reference adaptive consensus (MRACon), by arranging each agent a reference to track. Under this scheme, the consensus problem is divided into two parts, namely, the tracking of the output of the reference models for the uncertain agent dynamics and the consensus of the reference models themselves. The proposed algorithms can be implemented using the relative measurements in the absence of communication. Furthermore, the results have been extended to the cases of switching directed graphs, unknown control directions, and general linear uncertain multi-agent systems.