Article
Min–max MPC using a tractable QP problem
Author/s | Alamo, Teodoro
Rodríguez Ramírez, Daniel Muñoz de la Peña Sequedo, David Camacho, Eduardo F. |
Department | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Publication Date | 2007 |
Deposit Date | 2020-03-05 |
Published in |
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Abstract | 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. |
Citation | 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. |
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