This paper presents a constrained-control synthesis algorithm amenable for distributed multi-robot decision making. The proposed approach is based on a newly introduced controller selection method named analytic center selection which results in a dynamically defined controller that is able to ensures the satisfaction of state constraints. The controller synthesis lends itself to be distributed among a network of agents using state-of-the-art distributed optimization techniques. Resilience of the controller to imperfect communication channels among the robots is demonstrated in simulation.