Distributed model predictive control based on agent negotiation

In this paper we consider the control of several subsystems coupled through the inputs by a set of independent agents that are able to communicate. We assume that each agent has access only to the model and the state of one of the subsystems. This implies that in order to take a cooperative decision, the agents must negotiate. At each sampling time agents make proposals to improve an initial feasible solution on behalf of their local cost function, state and model. These proposals are accepted if the global cost improves the cost corresponding to the current solution. In addition, we provide conditions that guarantee that the closed-loop system is asymptotically stable along with an optimization based design procedure that is based on the full model of the system. Finally, the proposed scheme is put to test through simulation.