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Cited by 341

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

Learning Model Predictive Control for Iterative Tasks. A Data-Driven Control Framework

Open access
Rosolia, Ugo, Borrelli, Francesco·2018·IEEE Transactions on Automatic Control

Abstract

A learning model predictive controller for iterative tasks is presented. The controller is reference-free and is able to improve its performance by learning from previous iterations. A safe set and a terminal cost function are used in order to guarantee recursive feasibility and nondecreasing performance at each iteration. This paper presents the control design approach, and shows how to recursively construct terminal set and terminal cost from state and input trajectories of previous iterations. Simulation results show the effectiveness of the proposed control logic.

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Metadata

Authors
Rosolia, U., & Borrelli, F.
Venue
IEEE Transactions on Automatic Control
Published
2018-07
Volume
63
Issue
7
Pages
1883-1896
ISSN
  • 0018-9286 (ISSN-L)
  • 1558-2523
  • 2334-3303
OpenAlex
W2619028304
Cited by
341
References
27
FWCI
13.78
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Learning Model Predictive Control for Iterative Tasks. A Data-Driven Control Framework. https://app.cochat.ai/papers/10.1109/tac.2017.2753460
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Rosolia, U., & Borrelli, F. (2018). Learning Model Predictive Control for Iterative Tasks. A Data-Driven Control Framework. IEEE Transactions on Automatic Control. https://doi.org/10.1109/tac.2017.2753460

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.