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Artículo
A Robust Constrained Reference Governor Approach using Linear Matrix Inequalities
(Elsevier, 2009)
The purpose of this paper is to examine and provide a solution to the output reference tracking problem for uncertain systems subject to input saturation. As well-known, input saturation and modelling errors are very common ...
Ponencia
Enlarging the domain of attraction of MPC controller using invariant sets
(Elsevier, 2002)
This paper presents a method for enlarging the domain of attraction of nonlinear model predictive control (MPC). The useful way of guaranteeing stability of nonlinear MPC is to add a terminal constraint and a terminal cost ...
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 ...
Ponencia
MPC for tracking of piece-wise constant referente for constrained linear systems
(Elsevier, 2005)
Model predictive control (MPC) is one of the few techniques which is able to handle with constraints on both state and input of the plant. The admissible evolution and asymptotically convergence of the closed loop system ...