Online feedback optimization refers to the design of feedback controllers that asymptotically steer a physical system to the solution of an optimization problem while respecting physical and operational constraints. Here we are interested in exploring self-interested agents that do not want to cooperate for the sake of achieving a common goal but first and foremost have their own interest in mind. A relevant real-world example is selfish and uncoordinated congestion control by different power transmission system operators. We will investigate distributed Nash-seeking algorithms to solve the resulting antagonistic decision-making problems, and also deploy them in numerical and real-world case studies.
Online feedback optimization refers to the design of feedback controllers that asymptotically steer a physical system to the solution of an optimization problem while respecting physical and operational constraints. Here we are interested in exploring self-interested agents that do not want to cooperate for the sake of achieving a common goal but first and foremost have their own interest in mind. A relevant real-world example is selfish and uncoordinated congestion control by different power transmission system operators. We will investigate distributed Nash-seeking algorithms to solve the resulting antagonistic decision-making problems, and also deploy them in numerical and real-world case studies.