A distributed control law with guaranteed convergence rate for identically coupled linear systems

This paper investigates the stabilization and optimization problems for a group of identically linear agents with undirected interaction topology. It is shown that a distributed control law based on local measurements and relative information exchanged from neighboring agents can be designed for each agent to enable the agent states to be stabilized. Furthermore, due to the use of a parametric Lyapunov approach, the designed distributed control law guarantees not only optimization performance at a network level but also a convergence rate for the group of agents. Finally, a simulation example is provided to demonstrate the advantage as well as the effectiveness of the proposed method.