Artículo
Min–max MPC using a tractable QP problem
Autor/es | Alamo, Teodoro
Rodríguez Ramírez, Daniel Muñoz de la Peña Sequedo, David Camacho, Eduardo F. |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2007 |
Fecha de depósito | 2020-03-05 |
Publicado en |
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Resumen | Min–max model predictive controllers (MMMPC) suffer from a great computational burden that is often circumvented by using approximate solutions or upper bounds of the worst possible case of a performance index. This paper ... Min–max model predictive controllers (MMMPC) suffer from a great computational burden that is often circumvented by using approximate solutions or upper bounds of the worst possible case of a performance index. This paper proposes a computationally efficient MMMPC control strategy in which a close approximation of the solution of the min–max problem is computed using a quadratic programming problem. The overall computational burden is much lower than that of the min–max problem and the resulting control is shown to have a guaranteed stability. A simulation example is given in the paper. |
Cita | Álamo, T., Rodríguez Ramírez, D., Muñoz de la Peña Sequedo, D. y Fernández Camacho, E. (2007). Min–max MPC using a tractable QP problem. Automatica, 43 (4), 693-700. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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QP_min_maxSHORT2rev.pdf | 189.3Kb | [PDF] | Ver/ | |