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

• URL: http://arxiv.org/abs/2112.14676v1
• Date: Wed, 29 Dec 2021 17:51:48 GMT
• Title: Learning nonlinear dynamics in synchronization of knowledge-based leader-following networks
• Authors: Shimin Wang, Xiangyu Meng, Hongwei Zhang, Frank L. Lewis
• Abstract summary: This paper proposes a learning-based fully distributed observer for a class of nonlinear leader systems. We further synthesize an adaptive distributed control law for solving the leader-following synchronization problem of multiple Euler-Lagrange systems.
• Score: 16.670246060148617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge-based leader-following synchronization problem of heterogeneous nonlinear multi-agent systems is challenging since the leader's dynamic information is unknown to all follower nodes. 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. The class of leader dynamics considered here 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.

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