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Optimizing Prediction Dynamics With Saturated Inputs for Robust Model Predictive Control

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 paper presents a new method for overcoming this weakness, yielding a significant improvement in the degree of optimality, and achieving this with no increase in 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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https://hal-ifp.archives-ouvertes.fr/hal-03129828
Contributor : Catherine Belli <>
Submitted on : Wednesday, February 3, 2021 - 10:03:01 AM
Last modification on : Tuesday, February 9, 2021 - 3:06:52 AM

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Hoaï-Nam Nguyen. Optimizing Prediction Dynamics With Saturated Inputs for Robust Model Predictive Control. IEEE Transactions on Automatic Control, Institute of Electrical and Electronics Engineers, 2021, 66 (1), pp.383-390. ⟨10.1109/TAC.2020.2979399⟩. ⟨hal-03129828⟩

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