Abstract
This brief proposes a distributed model predictive control scheme based on satisficing theory in which the relative importance of each subsystem changes dynamically according to the current conditions. The controllers implement a situational altruism, where less satisfied controllers will automatically receive more importance. The global cost, instead of being fix as in other approaches, emerges from the cooperation process. The proposed control scheme guarantees both that the control input is Pareto-optimal and that each controller is guaranteed a minimum degree of optimality, even under coupled constraints. The main properties of the proposed approach are demonstrated through a traffic control example.
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