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

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 system...

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Bibliographic Details
Main Authors	Wang, Shimin, Meng, Xiangyu, Zhang, Hongwei, Lewis, Frank L
Format	Journal Article
Language	English
Published	29.12.2021
Subjects	Computer Science - Artificial Intelligence Computer Science - Systems and Control Mathematics - Optimization and Control Physics - Adaptation and Self-Organizing Systems
Online Access	Get full text
DOI	10.48550/arxiv.2112.14676

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Summary:	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.
DOI:	10.48550/arxiv.2112.14676