  We consider a network of autonomous agents whose outputs are actions in a
game with coupled constraints. In such network scenarios, agents seeking to
minimize coupled cost functions using distributed information while satisfying
the coupled constraints. Current methods consider the small class of
multi-integrator agents using primal-dual methods. These methods can only
ensure constraint satisfaction in steady-state. In contrast, we propose an
inexact penalty method using a barrier function for nonlinear agents with
equilibrium-independent passive dynamics. We show that these dynamics converge
to an epsilon-GNE while satisfying the constraints for all time, not only in
steady-state. We develop these dynamics in both the full-information and
partial-information settings. In the partial-information setting, dynamic
estimates of the others' actions are used to make decisions and are updated
through local communication. Applications to optical networks and velocity
synchronization of flexible robots are provided.
