Abstract

In this paper, we consider a network of autonomous agents with passive linear time-invariant dynamics involved in a game with coupled constraints. In such networked scenarios, agents have to make decisions compatible with seeking a generalized Nash equilibrium (GNE), while using networked information and satisfying the constraints. Existing methods are developed for multi-integrator agents only and furthermore, ensure the satisfaction of coupled constraints in steady-state only. We propose an inexact-penalty dynamics for passive LTI agents and show that it converges to an ε -GNE while ensuring the coupled constraints are met throughout the evolution of the agents' dynamics, not only in steady-state. Our scheme is developed for both the full-decision information setting and the partial-decision one. In the partial-information setting, each agent makes its decision based on a dynamic estimate of the others' states, updated by local communication with its neighbours, which offsets the lack of global information. Applications to optical networks are provided.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call