Learning nonlinear dynamics in synchronization of knowledge-based leader-following networks
This addresses synchronization in multi-agent systems where the leader's dynamics are uncertain, which is incremental as it builds on existing distributed control methods.
The paper tackles the problem of synchronizing heterogeneous nonlinear multi-agent systems with an unknown leader's dynamics by proposing a learning-based distributed observer that simultaneously learns the leader's dynamics and states, and synthesizes an adaptive control law for leader-following synchronization of Euler-Lagrange systems, demonstrated through simulation.
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.