Document Type
Article
Publication Date
8-1-2024
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.
Publication Source (Journal or Book title)
Automatica
Recommended Citation
Wang, S., Meng, X., Zhang, H., & Lewis, F. (2024). Learning nonlinear dynamics in synchronization of knowledge-based leader-following networks. Automatica, 166 https://doi.org/10.1016/j.automatica.2024.111695
