Ponencia
Min-max model predictive control as a quadratic program
Autor/es | Muñoz de la Peña Sequedo, David
Alamo, Teodoro Rodríguez Ramírez, Daniel Camacho, Eduardo F. |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2005 |
Fecha de depósito | 2020-03-24 |
Publicado en |
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ISBN/ISSN | 1474-6670 |
Resumen | This paper deals with the implementation of min-max model predictive control for constrained linear systems with bounded additive uncertainties and quadratic cost functions. This type of controller has been shown to be a ... This paper deals with the implementation of min-max model predictive control for constrained linear systems with bounded additive uncertainties and quadratic cost functions. This type of controller has been shown to be a continuous piecewise affine function of the state vector by geometrical methods. However, no algorithm for computing the explicit solution has been given. In this paper, we show that the min-max optimization problem can be expressed as a multi-parametric quadratic program, and so, the explicit form of the controller may be determined by standard multi-parametric techniques. |
Cita | Muñoz de la Peña Sequedo, D., Alamo, T., Rodríguez Ramírez, D. y Camacho, E.F. (2005). Min-max model predictive control as a quadratic program. En Triennial World Congress (263-268), Praga: Elsevier. |
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