Model-independent rendezvous of Euler-Lagrange agents on directed networks
Mengbin Ye, Changbin Brad Yu, Brian D. O. Anderson · 2015
This paper proposes a distributed, modelindependent algorithm to achieve rendezvous to a stationary leader for a directed network where each fully-actuated agent has Euler-Lagrange self-dynamics. We show that if the directed graph contains a directed spanning tree, with the leader as the root node and with no incoming edges, then a model-independent algorithm semi-globally achieves the rendezvous objective exponentially fast. By model-independent we mean that each agent can execute the algorithm with no knowledge of the parameters of the self-dynamics of any agent in the network. For stability, a pair of control gain terms for each agent are required to meet several inequalities and so design of the algorithm requires some limited knowledge of global information. Numerical simulations are provided to illustrate the algorithm's effectiveness.