Learning nonlinear dynamics in synchronization of knowledge-based leader-following networks

TL;DR

This paper introduces a learning-based distributed observer for nonlinear leader systems in multi-agent networks, enabling followers to learn leader dynamics and achieve synchronization despite uncertainties.

Contribution

It presents a novel fully distributed observer that learns general nonlinear leader dynamics without bounded Jacobian assumptions, and an adaptive control law for Euler-Lagrange systems.

Findings

Successful simulation demonstration of the proposed method.

Effective learning of leader dynamics in a fully distributed manner.

Achieved leader-following synchronization despite nonlinear uncertainties.

Abstract

Knowledge-based leader-following synchronization of heterogeneous nonlinear multi-agent systems is a challenging problem since the leader's dynamic information is unknown to any follower node. This paper proposes a learning-based fully distributed observer for a class of nonlinear leader systems, which can simultaneously learn the leader's dynamics and states. This class of leader dynamics is rather general and does not require a bounded Jacobian matrix. Based on this learning-based distributed observer, we further synthesize an adaptive distributed control law for solving the leader-following synchronization problem of multiple Euler-Lagrange systems subject to an uncertain nonlinear leader system. The results are illustrated by a simulation example.