Julian Barreiro-Gomez

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Population dynamics have been widely used in the design of learning and control systems for networked engineering applications, where the information dependency among elements of the network has become a relevant issue. Classic population dynamics (e.g., replicator, logit choice, Smith, and projection) require full information to evolve to the solution(More)
Model predictive control (MPC) is one of the most used optimizationbased control strategies for large-scale systems, since this strategy allows to consider a large number of states and multi-objective cost functions in a straightforward way. One of the main issues in the design of multi-objective MPC controllers, which is the tuning of the weights(More)
This paper proposes a novel methodology for solving constrained optimization problems in a distributed way, inspired by population dynamics and adding dynamics to the population masses. The proposed methodology divides the problem into smaller problems, whose feasible regions vary over time achieving an agreement to solve the global problem. The methodology(More)
Large-scale network systems involve a large number of variables, making the design of real-time controllers challenging. A distributed controller design allows to reduce computational requirements since tasks may be divided into different subsystems, making possible to guarantee real-time processing. This paper proposes a constrained optimizationbased(More)
This paper proposes a non–centralized Model Predictive Control (MPC) scheme for a system comprised by several sub-systems. Operational constraints for each sub–system are considered as well as a single coupled constraint on the control inputs that models a limitation of the resource supplied by the controller. If the underlying optimization problem is of(More)
Population games have become a powerful tool for solving resource-allocation problems in a distributed manner, and for the design of non-centralized optimization-based controllers. The aim of this paper is to illustrate the advantages of two recently introduced population-game approaches in comparison to other classical optimization methods. More(More)
Model predictive control (MPC) is a suitable strategy for the control of large-scale systems that have multiple design requirements, e.g., multiple physical and operational constraints. Besides, an MPC controller is able to deal with multiple control objectives considering them within the cost function, which implies to determine a proper prioritization for(More)