Two-Scale Model Predictive Control for Resource Optimization Problems With Switched Decisions
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Otros documentos de la autoría: Balaguer, Pedro; Alfonso, j. Carlos; Martinez Marquez, Camilo Itzame; Martínez Navarro, Germán; Orts-Grau, Salvador; Segui-Chilet, Salvador
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7034
comunitat-uji-handle3:10234/8619
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Título
Two-Scale Model Predictive Control for Resource Optimization Problems With Switched DecisionsAutoría
Fecha de publicación
2022-05-30Editor
IEEECita bibliográfica
BALAGUER-HERRERO, Pedro, et al. Two-Scale Model Predictive Control for Resource Optimization Problems with Switched Decisions. IEEE Access, 2022.Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Model predictive control (MPC) is widely used in resource optimization problems because it
naturally deals with bounded controls and states and allows predictive information to be included. However,
at each sampling ... [+]
Model predictive control (MPC) is widely used in resource optimization problems because it
naturally deals with bounded controls and states and allows predictive information to be included. However,
at each sampling instant, an optimization problem must be solved. Resource optimization problems with
switching control actions naturally lead to optimization problems with integer decision variables, which
are computationally costly, particularly when the number of variables is large. As a result, the approach of
directly discretizing (DD) the problem to derive a mixed-integer linear program (MILP) sets fundamental
limitations on the MPC sampling rate owing to the computational time required to solve the optimization
problem. In this paper, we propose a two-scale optimization algorithm (TSOA) for MPC. On the first-scale,
the entire prediction horizon is considered and the algorithm provides the optimal resources to be used at each
interval with a constant weighting cost. This optimization problem may be cast as a linear program (LP);
thus, it is computationally tractable even for a large number of variables and constraints. In the secondscale, the switching nature of the decision variable is recovered by posing an MILP to deploy the optimal
resources computed in the previous scale. In this manner, the MILP is solved for a shorter time interval
than the entire prediction horizon, thus reducing the number of variables in the optimization problem. The
simulation results demonstrate the computational advantages of the proposed algorithm compared to direct
problem discretization and optimization. [-]
Publicado en
IEEE Access, 2022Derechos de acceso
info:eu-repo/semantics/openAccess
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