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Robust Learning Model Predictive Control for Linear Systems Performing Iterative Tasks

Rosolia, Ugo and Zhang, Xiaojing and Borrelli, Francesco (2021) Robust Learning Model Predictive Control for Linear Systems Performing Iterative Tasks. IEEE Transactions on Automatic Control . ISSN 0018-9286. (In Press) https://resolver.caltech.edu/CaltechAUTHORS:20210528-094924698

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Abstract

A robust Learning Model Predictive Controller (LMPC) for uncertain systems performing iterative tasks is presented. At each iteration of the control task the closed-loop state, input and cost are stored and used in the controller design. This paper first illustrates how to construct robust control invariant sets and safe control policies exploiting historical data. Then, we propose an iterative LMPC design procedure, where data generated by a robust controller at iteration j are used to design a robust LMPC at the next j+1 iteration. We show that this procedure allows us to iteratively enlarge the domain of the control policy and it guarantees recursive constraints satisfaction, input to state stability and performance bounds for the certainty equivalent closed-loop system. The use of different feedback policies along the horizon is the key element of the proposed design. The effectiveness of the proposed control scheme is illustrated on a linear system subject to bounded additive disturbance.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/tac.2021.3083559DOIArticle
https://arxiv.org/abs/1911.09234arXivDiscussion Paper
ORCID:
AuthorORCID
Rosolia, Ugo0000-0002-1682-0551
Borrelli, Francesco0000-0001-8919-6430
Alternate Title:Robust Learning Model Predictive Control for Linear Systems
Additional Information:© 2021 IEEE. The authors would like to thank Monimoy Bujarbaruah and Siddharth Nair for helpful discussions and reviews. Some of the research described in this review was funded by the Hyundai Center of Excellence at the University of California, Berkeley. This work was also sponsored by the Office of Naval Research. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Office of Naval Research or the US government.
Funders:
Funding AgencyGrant Number
University of California, BerkeleyUNSPECIFIED
Office of Naval Research (ONR)UNSPECIFIED
Record Number:CaltechAUTHORS:20210528-094924698
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210528-094924698
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109299
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:28 May 2021 20:35
Last Modified:28 May 2021 20:35

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