Title :
Efficient implementation of min-max model predictive control with bounded uncertainties
Author :
Álamo, T. ; Ramírez, D.R. ; Camacho, E.F.
Author_Institution :
Departamento de Ingenieria de Sistemas y Automatica, Univ. de Sevilla, Spain
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 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.
Keywords :
minimax techniques; predictive control; MMMPC; Min-Max Model Predictive Control; bounded uncertainties; control models; min-max problem; numerical optimization; piecewise affine; reduced min-max problem; Approximation error; Contracts; Costs; Equations; Mathematical model; Neural networks; Predictive control; Predictive models; Sampling methods; Uncertainty;
Conference_Titel :
Control Applications, 2002. Proceedings of the 2002 International Conference on
Print_ISBN :
0-7803-7386-3
DOI :
10.1109/CCA.2002.1038677
