  This paper focuses on a passivity-based distributed reference governor (RG)
applied to a pre-stabilized mobile robotic network. The novelty of this paper
lies in the method used to solve the RG problem, where a passivity-based
distributed optimization scheme is proposed. In particular, the gradient
descent method minimizes the global objective function while the dual ascent
method maximizes the Hamiltonian. To make the agents converge to the agreed
optimal solution, a proportional-integral consensus estimator is used. This
paper proves the convergence of the state estimates of the RG to the optimal
solution through passivity arguments, considering the physical system static.
Then, the effectiveness of the scheme considering the dynamics of the physical
system is demonstrated through simulations and experiments.
