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

arXiv: https://arxiv.org/abs/2112.14676 · HTML: https://mlsanity.com/paper/2112.14676/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 29 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Adaptive Leader-Following Consensus for Multiple Euler-Lagrange Systems with an Uncertain Leader System](https://mlsanity.com/paper/1909.07851/) (2019)
- [Robust output synchronization of a network of heterogeneous nonlinear agents via nonlinear regulation theory](https://mlsanity.com/paper/1303.2804/) (2013)
- [Designing Fully Distributed Consensus Protocols for Linear Multi-agent Systems with Directed Graphs](https://mlsanity.com/paper/1312.7377/) (2013)
- [Distributed adaptive stabilization](https://mlsanity.com/paper/2105.14004/) (2021)

## Built on by (most cited first, 1 of 1)

- [COIN-GP: Cooperative Online Learning in Networked Distributed Systems with Partial Measurements via Gaussian Process Regression](https://mlsanity.com/paper/2609.20598/) (2026)

## Abstract (arXiv)

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.