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Linear robust adaptive model predictive control: Computational complexity and conservatism

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Original languageEnglish
Title of host publication2019 IEEE 58th Conference on Decision and Control (CDC)
Subtitle of host publicationProceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1383-1388
Number of pages6
ISBN (electronic)978-1-7281-1398-2
ISBN (print)978-1-7281-1399-9
Publication statusPublished - Dec 2019
Event2019 IEEE 58th Conference on Decision and Control (CDC) - Nice, France
Duration: 11 Dec 201913 Dec 2019

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2019-December
ISSN (Print)0743-1546
ISSN (electronic)2576-2370

Abstract

In this paper, we present a robust adaptive model predictive control (MPC) scheme for linear systems subject to parametric uncertainty and additive disturbances. The proposed approach provides a computationally efficient formulation with theoretical guarantees (constraint satisfaction and stability), while allowing for reduced conservatism and improved performance due to online parameter adaptation. A moving window parameter set identification is used to compute a fixed complexity parameter set based on past data. Robust constraint satisfaction is achieved by using a computationally efficient tube based robust MPC method. The predicted cost function is based on a least mean squares point estimate, which ensures finite-gain Ⅎ2 stability of the closed loop. The overall algorithm has a fixed (user specified) computational complexity. We illustrate the applicability of the approach and the trade-off between conservatism and computational complexity using a numerical example.

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Cite this

Linear robust adaptive model predictive control: Computational complexity and conservatism. / Köhler, Johannes; Andina, Elisa; Soloperto, Raffaele et al.
2019 IEEE 58th Conference on Decision and Control (CDC): Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1383-1388 9028970 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2019-December).

Research output: Chapter in book/report/conference proceedingConference contributionResearch

Köhler, J, Andina, E, Soloperto, R, Muller, MA & Allgöwer, F 2019, Linear robust adaptive model predictive control: Computational complexity and conservatism. in 2019 IEEE 58th Conference on Decision and Control (CDC): Proceedings., 9028970, Proceedings of the IEEE Conference on Decision and Control, vol. 2019-December, Institute of Electrical and Electronics Engineers Inc., pp. 1383-1388, 2019 IEEE 58th Conference on Decision and Control (CDC), 11 Dec 2019. https://doi.org/10.1109/CDC40024.2019.9028970
Köhler, J., Andina, E., Soloperto, R., Muller, M. A., & Allgöwer, F. (2019). Linear robust adaptive model predictive control: Computational complexity and conservatism. In 2019 IEEE 58th Conference on Decision and Control (CDC): Proceedings (pp. 1383-1388). Article 9028970 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2019-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC40024.2019.9028970
Köhler J, Andina E, Soloperto R, Muller MA, Allgöwer F. Linear robust adaptive model predictive control: Computational complexity and conservatism. In 2019 IEEE 58th Conference on Decision and Control (CDC): Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1383-1388. 9028970. (Proceedings of the IEEE Conference on Decision and Control). doi: 10.1109/CDC40024.2019.9028970
Köhler, Johannes ; Andina, Elisa ; Soloperto, Raffaele et al. / Linear robust adaptive model predictive control : Computational complexity and conservatism. 2019 IEEE 58th Conference on Decision and Control (CDC): Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1383-1388 (Proceedings of the IEEE Conference on Decision and Control).
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title = "Linear robust adaptive model predictive control: Computational complexity and conservatism",
abstract = "In this paper, we present a robust adaptive model predictive control (MPC) scheme for linear systems subject to parametric uncertainty and additive disturbances. The proposed approach provides a computationally efficient formulation with theoretical guarantees (constraint satisfaction and stability), while allowing for reduced conservatism and improved performance due to online parameter adaptation. A moving window parameter set identification is used to compute a fixed complexity parameter set based on past data. Robust constraint satisfaction is achieved by using a computationally efficient tube based robust MPC method. The predicted cost function is based on a least mean squares point estimate, which ensures finite-gain Ⅎ2 stability of the closed loop. The overall algorithm has a fixed (user specified) computational complexity. We illustrate the applicability of the approach and the trade-off between conservatism and computational complexity using a numerical example.",
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AU - Muller, Matthias A.

AU - Allgöwer, Frank

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AB - In this paper, we present a robust adaptive model predictive control (MPC) scheme for linear systems subject to parametric uncertainty and additive disturbances. The proposed approach provides a computationally efficient formulation with theoretical guarantees (constraint satisfaction and stability), while allowing for reduced conservatism and improved performance due to online parameter adaptation. A moving window parameter set identification is used to compute a fixed complexity parameter set based on past data. Robust constraint satisfaction is achieved by using a computationally efficient tube based robust MPC method. The predicted cost function is based on a least mean squares point estimate, which ensures finite-gain Ⅎ2 stability of the closed loop. The overall algorithm has a fixed (user specified) computational complexity. We illustrate the applicability of the approach and the trade-off between conservatism and computational complexity using a numerical example.

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