Robust and Stable Predictive Control with Bounded Uncertainties
[EN] Min-Max optimization is often used for improving robustness in Model Predictive Control (MPC). An analogy to this optimization could be the BDU (Bounded Data Uncertainties) method, which is a regularization technique for least-squares problems that takes into account the uncertainty bounds. Sta...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2008 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/125223 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/125223 |
| Access Level: | acceso abierto |
| Palabra clave: | Model predictive control Min-max optimization Regularization Robustness Stability INGENIERIA DE SISTEMAS Y AUTOMATICA |
| Sumario: | [EN] Min-Max optimization is often used for improving robustness in Model Predictive Control (MPC). An analogy to this optimization could be the BDU (Bounded Data Uncertainties) method, which is a regularization technique for least-squares problems that takes into account the uncertainty bounds. Stability of MPC can be achieved by using terminal constraints, such as in the CRHPC (Constrained Receding-Horizon Predictive Control) algorithm. By combining both BDU and CRHPC methods, a robust and stable MPC is obtained, which is the aim of this work. BDU also offers a guided method of tuning the empirically tuned penalization parameter for the control effort in MPC. (C) 2008 Elsevier Inc. All rights reserved. |
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