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Mostrando ítems 1-6 de 6
Ponencia
Neural Network Based Min-Max Predictive Control. Application to a Heat Exchanger
(Elsevier, 2001)
Min-max model predictive controllers (MMMPC) have been proposed for the control of linear plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the numerical ...
Artículo
Implementation of min–max MPC using hinging hyperplanes. Application to a heat exchanger
(Elsevier, 2004)
Min–max model predictive control (MMMPC) is one of the few control techniques able to cope with modelling errors or uncertainties in an explicit manner. The implementation of MMMPC suffers a large computational burden due ...
Ponencia
Min-Max Predictive Control of a Pilot Plant using a QP Approach
(Institute of Electrical and Electronics Engineers (IEEE), 2008)
The practical implementation of min-max MPC (MMMPC) controllers is limited by the computational burden required to compute the control law. This problem can be circumvented by using approximate solutions or upper bounds ...
Ponencia
Computationally efficient min-max MPC
(Elsevier, 2005)
Min-Max MPC (MMMPC) controllers (Campo and Morari, 1987) suffer from a great computational burden that is often circumvented by using upper bounds of the worst possible case of a performance index. These upper bounds are ...
Artículo
Min-Max MPC based on a computationally efficient upper bound of the worst case cost
(Elsevier, 2006)
Min-Max MPC (MMMPC) controllers [P.J. Campo, M. Morari, Robust model predictive control, in: Proc. American Control Conference, June 10–12, 1987, pp. 1021–1026] suffer from a great computational burden which limits their ...
Ponencia
Min-max model predictive control as a quadratic program
(Elsevier, 2005)
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 ...