Article
Computational burden reduction in Min-Max MPC
Author/s | Rodríguez Ramírez, Daniel
Alamo, Teodoro Camacho, Eduardo F. |
Department | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Publication Date | 2011 |
Deposit Date | 2020-03-31 |
Published in |
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Abstract | Min–max model predictive control (MMMPC) is one of the strategies used to control plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the complex numerical ... Min–max model predictive control (MMMPC) is one of the strategies used to control plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the complex numerical optimization problem that has to be solved at every sampling time. This paper shows how to overcome this by transforming the original problem into a reduced min–max problem whose solution is much simpler. In this way, the range of processes to which MMMPC can be applied is considerably broadened. Proofs based on the properties of the cost function and simulation examples are given in the paper. |
Citation | Rodríguez Ramírez, D., Alamo, T. y Camacho, E.F. (2011). Computational burden reduction in Min-Max MPC. Journal of the Franklin Institute, 348 (9), 2430-2447. |