Two-Scale Model Predictive Control for Resource Optimization Problems With Switched Decisions
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Altres documents de l'autoria: Balaguer, Pedro; Alfonso, j. Carlos; Martinez Marquez, Camilo Itzame; Martínez Navarro, Germán; Orts-Grau, Salvador; Segui-Chilet, Salvador
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Two-Scale Model Predictive Control for Resource Optimization Problems With Switched DecisionsAutoria
Data de publicació
2022-05-30Editor
IEEECita bibliogràfica
BALAGUER-HERRERO, Pedro, et al. Two-Scale Model Predictive Control for Resource Optimization Problems with Switched Decisions. IEEE Access, 2022.Tipus de document
info:eu-repo/semantics/articleVersió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
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. [-]
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IEEE Access, 2022Drets d'accés
info:eu-repo/semantics/openAccess
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