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A Comparative Study of Stochastic Model Predictive Controllers

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A Comparative Study of Stochastic Model Predictive Controllers

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dc.contributor.author Gonzalez, Edwin es_ES
dc.contributor.author Sanchís Saez, Javier es_ES
dc.contributor.author Garcia-Nieto, Sergio es_ES
dc.contributor.author Salcedo-Romero-de-Ávila, José-Vicente es_ES
dc.date.accessioned 2021-05-21T03:32:03Z
dc.date.available 2021-05-21T03:32:03Z
dc.date.issued 2020-12 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166589
dc.description.abstract [EN] A comparative study of two state-of-the-art stochastic model predictive controllers for linear systems with parametric and additive uncertainties is presented. On the one hand, Stochastic Model Predictive Control (SMPC) is based on analytical methods and solves an optimal control problem (OCP) similar to a classic Model Predictive Control (MPC) with constraints. SMPC defines probabilistic constraints on the states, which are transformed into equivalent deterministic ones. On the other hand, Scenario-based Model Predictive Control (SCMPC) solves an OCP for a specified number of random realizations of uncertainties, also called scenarios. In this paper, Classic MPC, SMPC and SCMPC are compared through two numerical examples. Thanks to several Monte-Carlo simulations, performances of classic MPC, SMPC and SCMPC are compared using several criteria, such as number of successful runs, number of times the constraints are violated, integral absolute error and computational cost. Moreover, a Stochastic Model Predictive Control Toolbox was developed by the authors, available on MATLAB Central, in which it is possible to simulate a SMPC or a SCMPC to control multivariable linear systems with additive disturbances. This software was used to carry out part of the simulations of the numerical examples in this article and it can be used for results reproduction. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Electronics es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Model predictive control (MPC) es_ES
dc.subject Scenario-based model predictive control (SCMPC) es_ES
dc.subject Stochastic model predictive control (SMPC) es_ES
dc.subject Chance constraints es_ES
dc.subject Parametric and additive uncertainties es_ES
dc.subject Additive disturbances es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.title A Comparative Study of Stochastic Model Predictive Controllers es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/electronics9122078 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Máquinas y Motores Térmicos - Departament de Màquines i Motors Tèrmics es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica es_ES
dc.description.bibliographicCitation Gonzalez, E.; Sanchís Saez, J.; Garcia-Nieto, S.; Salcedo-Romero-De-Ávila, J. (2020). A Comparative Study of Stochastic Model Predictive Controllers. Electronics. 9(12):1-22. https://doi.org/10.3390/electronics9122078 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/electronics9122078 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 22 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.description.issue 12 es_ES
dc.identifier.eissn 2079-9292 es_ES
dc.relation.pasarela S\423393 es_ES


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