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Published February 2022 | Accepted Version
Journal Article Open

Robust Learning Model-Predictive Control for Linear Systems Performing Iterative Tasks

Abstract

In this article, 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 article 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 iteration j+1. 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 disturbances.

Additional Information

© 2021 IEEE. Manuscript received June 20, 2020; revised January 7, 2021; accepted May 8, 2021. Date of publication May 25, 2021; date of current version January 28, 2022. This work was supported in this research was founded only by the Office of Naval Research. Recommended by Associate Editor L. Zhang. 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 U.S. government. The authors would like to thank Monimoy Bujarbaruah and Siddharth Nair for helpful discussions and reviews.

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Accepted Version - 1911.09234.pdf

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Additional details

Created:
August 20, 2023
Modified:
October 23, 2023