Efficient implementation of min-max model predictive control with bounded uncertainties

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...

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Bibliographic Details
Published in2002 IEEE International Conference on Control Applications Vol. 2; pp. 651 - 656 vol.2
Main Authors Alamo, T., Ramirez, D.R., Camacho, E.F.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2002
Subjects
Approximation error
Contracts
Costs
Equations
Mathematical model
Neural networks
Predictive control
Predictive models
Sampling methods
Uncertainty
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ISBN0780373863
9780780373860
DOI10.1109/CCA.2002.1038677

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Summary: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.
ISBN:0780373863
9780780373860
DOI:10.1109/CCA.2002.1038677