Optimizing Prediction Dynamics with Saturated Inputs for Robust Model Predictive Control

Hoai Nam Nguyen

doi:10.1109/TAC.2020.2979399

Optimizing Prediction Dynamics with Saturated Inputs for Robust Model Predictive Control

Hoai Nam Nguyen

IFP Energies nouvelles

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23 Citations (Scopus)

Résumé

A model predictive control algorithm based on offline optimization of prediction dynamics enables an efficient online computation. However, the price for this efficiency is a reduction in the degree of optimality. This article presents a new method for overcoming this weakness, yielding a significant improvement in the degree of optimality, and achieving this with no increase in an online computational load. Two numerical examples with comparison to earlier solutions from the literature illustrate the effectiveness of the proposed algorithm.

langue originale	Anglais
Numéro d'article	9027879
Pages (de - à)	383-390
Nombre de pages	8
journal	IEEE Transactions on Automatic Control
Volume	66
Numéro de publication	1
Les DOIs	https://doi.org/10.1109/TAC.2020.2979399
état	Publié - 1 janv. 2021
Modification externe	Oui

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10.1109/TAC.2020.2979399

Contient cette citation

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abstract = "A model predictive control algorithm based on offline optimization of prediction dynamics enables an efficient online computation. However, the price for this efficiency is a reduction in the degree of optimality. This article presents a new method for overcoming this weakness, yielding a significant improvement in the degree of optimality, and achieving this with no increase in an online computational load. Two numerical examples with comparison to earlier solutions from the literature illustrate the effectiveness of the proposed algorithm.",

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KW - linear matrix inequalities

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KW - predictive control

KW - uncertain systems

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Optimizing Prediction Dynamics with Saturated Inputs for Robust Model Predictive Control

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