A robust gradient-based MPC for integrating real time optimizer (RTO) with control
A gradient-based model predictive control (MPC) strategy was recently proposed to reduce the computational burden derived from the explicit inclusion of an economic real time optimization (RTO). The main idea is to compute a suboptimal solution, which is the convex combination of a feasible solution...
| Authors: | ; ; |
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| Format: | Article |
| Status: | Published version |
| Publication date: | 2017 |
| Country: | Argentina |
| Institution: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repository: | CONICET Digital (CONICET) |
| Language: | English |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/46943 |
| Online Access: | http://hdl.handle.net/11336/46943 |
| Access Level: | Open access |
| Keyword: | Model Predictive Control Economic Optimization Robust Control https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 |
| _version_ | 1878403363161243648 |
|---|---|
| author | D'jorge, Agustina |
| author2 | Ferramosca, Antonio González, Alejandro Hernán |
| author2_role | author author |
| author_browse | D'jorge, Agustina Ferramosca, Antonio González, Alejandro Hernán |
| author_facet | D'jorge, Agustina Ferramosca, Antonio González, Alejandro Hernán |
| author_role | author |
| collection | CONICET Digital (CONICET) |
| description | A gradient-based model predictive control (MPC) strategy was recently proposed to reduce the computational burden derived from the explicit inclusion of an economic real time optimization (RTO). The main idea is to compute a suboptimal solution, which is the convex combination of a feasible solution and a solution of an approximated (linearized) problem. The main benefits of this strategy are that convergence is still guaranteed and good economic performances are obtained, according to several simulation scenarios. The formulation, however, is developed only for the nominal case, which significantly reduces its applicability. In this work, an extension of the gradient-based MPC to explicitly account for disturbances is made. The resulting robust formulation considers a nominal prediction model, but restricted constraints (in order to account for the effect of additive disturbances). The nominal economic performance is preserved and robust stability is ensured. An illustrative example shows the benefits of the proposal. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | AR_705d0cb2b4adf736f2e6a16a837e85c9 |
| identifier_str_mv | D'jorge, Agustina; Ferramosca, Antonio; González, Alejandro Hernán; A robust gradient-based MPC for integrating real time optimizer (RTO) with control; Elsevier; Journal Of Process Control; 54; 6-2017; 65-80 0959-1524 CONICET Digital CONICET |
| institution | Consejo Nacional de Investigaciones Científicas y Técnicas |
| instname_str | Consejo Nacional de Investigaciones Científicas y Técnicas |
| language | eng |
| network_acronym_str | AR |
| network_name_str | Argentina |
| oai_identifier_str | oai:ri.conicet.gov.ar:11336/46943 |
| publishDate | 2017 |
| publishDateSort | 2017 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| reponame_str | CONICET Digital (CONICET) |
| spelling | A robust gradient-based MPC for integrating real time optimizer (RTO) with control D'jorge, Agustina Ferramosca, Antonio González, Alejandro Hernán Model Predictive Control Economic Optimization Robust Control https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 A gradient-based model predictive control (MPC) strategy was recently proposed to reduce the computational burden derived from the explicit inclusion of an economic real time optimization (RTO). The main idea is to compute a suboptimal solution, which is the convex combination of a feasible solution and a solution of an approximated (linearized) problem. The main benefits of this strategy are that convergence is still guaranteed and good economic performances are obtained, according to several simulation scenarios. The formulation, however, is developed only for the nominal case, which significantly reduces its applicability. In this work, an extension of the gradient-based MPC to explicitly account for disturbances is made. The resulting robust formulation considers a nominal prediction model, but restricted constraints (in order to account for the effect of additive disturbances). The nominal economic performance is preserved and robust stability is ensured. An illustrative example shows the benefits of the proposal. Fil: D'jorge, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Ferramosca, Antonio. Universidad Tecnológica Nacional; Argentina Fil: González, Alejandro Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Elsevier http://hdl.handle.net/11336/46943 D'jorge, Agustina; Ferramosca, Antonio; González, Alejandro Hernán; A robust gradient-based MPC for integrating real time optimizer (RTO) with control; Elsevier; Journal Of Process Control; 54; 6-2017; 65-80 0959-1524 CONICET Digital CONICET |
| spellingShingle | A robust gradient-based MPC for integrating real time optimizer (RTO) with control D'jorge, Agustina Model Predictive Control Economic Optimization Robust Control https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 |
| status_str | publishedVersion |
| title | A robust gradient-based MPC for integrating real time optimizer (RTO) with control |
| title_full | A robust gradient-based MPC for integrating real time optimizer (RTO) with control |
| title_fullStr | A robust gradient-based MPC for integrating real time optimizer (RTO) with control |
| title_full_unstemmed | A robust gradient-based MPC for integrating real time optimizer (RTO) with control |
| title_short | A robust gradient-based MPC for integrating real time optimizer (RTO) with control |
| title_sort | A robust gradient-based MPC for integrating real time optimizer (RTO) with control |
| topic | Model Predictive Control Economic Optimization Robust Control https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 |
| topic_facet | Model Predictive Control Economic Optimization Robust Control https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 |
| url | http://hdl.handle.net/11336/46943 |