Robust Model Predictive Control Based on Stabilizing Parameter Space Calculus

Ilhan Mutlu; Juraj Oravec; Frank Schrodel; Rick Vobwinkel; Monika Bakosova; Mehmet Turan Soylemez

doi:10.23919/ECC.2018.8550618

Robust Model Predictive Control Based on Stabilizing Parameter Space Calculus

Ilhan Mutlu, Juraj Oravec, Frank Schrodel, Rick Vobwinkel, Monika Bakosova, Mehmet Turan Soylemez

Department of Control and Automation Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The aim of this study is to introduce a novel approach for robust model predictive control (MPC) design based on stabilizing parameter spaces. In order to determine the stabilizing parameter regions, a Lyapunov equation based approach is proposed for nominal systems. In addition to the free controller parameters, it is also possible to determine boundaries of uncertain parameters in the present approach. The precomputed stability conditions on controller parameters are inserted to the MPC problem formulation as constraints. By this way, the stability of the closed-loop system is ensured. The proposed approach allows to design the nominal MPC, instead of the robust one. Using the predetermined constraints, the MPC is implemented to optimize the controller parameters over this stabilizing set. This paper introduces three particular control scenarios that tune the basic properties of the novel approach, e.g., runtime and computational effort. Two illustrative case studies are presented to demonstrate the efficiency of the proposed robust MPC strategy.

Original language	English
Title of host publication	2018 European Control Conference, ECC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	206-212
Number of pages	7
ISBN (Electronic)	9783952426982
DOIs	https://doi.org/10.23919/ECC.2018.8550618
Publication status	Published - 27 Nov 2018
Event	16th European Control Conference, ECC 2018 - Limassol, Cyprus Duration: 12 Jun 2018 → 15 Jun 2018

Publication series

Name	2018 European Control Conference, ECC 2018

Conference

Conference	16th European Control Conference, ECC 2018
Country/Territory	Cyprus
City	Limassol
Period	12/06/18 → 15/06/18

Bibliographical note

Publisher Copyright:
© 2018 European Control Association (EUCA).

Funding

ACKNOWLEDGMENT J. Oravec and M. Bakosˇová gratefully acknowledge the contribution of the Scientific Grant Agency of the Slovak Republic under the grant 1/0112/16 and the Slovak Research and Development Agency under the project APVV-15-0007. R. Voßwinkel gratefully acknowledge the financial support of this work by the German Academic Scholarship Foundation.

Funders	Funder number
German Academic Scholarship Foundation
Agentúra na Podporu Výskumu a Vývoja	APVV-15-0007
Vedecká Grantová Agentúra MŠVVaŠ SR a SAV	1/0112/16

Access to Document

10.23919/ECC.2018.8550618

Cite this

Mutlu, I., Oravec, J., Schrodel, F., Vobwinkel, R., Bakosova, M., & Soylemez, M. T. (2018). Robust Model Predictive Control Based on Stabilizing Parameter Space Calculus. In 2018 European Control Conference, ECC 2018 (pp. 206-212). Article 8550618 (2018 European Control Conference, ECC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ECC.2018.8550618

@inproceedings{36bda2ed05cc4a9b8d60f32a502fee00,

title = "Robust Model Predictive Control Based on Stabilizing Parameter Space Calculus",

abstract = "The aim of this study is to introduce a novel approach for robust model predictive control (MPC) design based on stabilizing parameter spaces. In order to determine the stabilizing parameter regions, a Lyapunov equation based approach is proposed for nominal systems. In addition to the free controller parameters, it is also possible to determine boundaries of uncertain parameters in the present approach. The precomputed stability conditions on controller parameters are inserted to the MPC problem formulation as constraints. By this way, the stability of the closed-loop system is ensured. The proposed approach allows to design the nominal MPC, instead of the robust one. Using the predetermined constraints, the MPC is implemented to optimize the controller parameters over this stabilizing set. This paper introduces three particular control scenarios that tune the basic properties of the novel approach, e.g., runtime and computational effort. Two illustrative case studies are presented to demonstrate the efficiency of the proposed robust MPC strategy.",

author = "Ilhan Mutlu and Juraj Oravec and Frank Schrodel and Rick Vobwinkel and Monika Bakosova and Soylemez, {Mehmet Turan}",

note = "Publisher Copyright: {\textcopyright} 2018 European Control Association (EUCA).; 16th European Control Conference, ECC 2018 ; Conference date: 12-06-2018 Through 15-06-2018",

year = "2018",

month = nov,

day = "27",

doi = "10.23919/ECC.2018.8550618",

language = "English",

series = "2018 European Control Conference, ECC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "206--212",

booktitle = "2018 European Control Conference, ECC 2018",

address = "United States",

}

Mutlu, I, Oravec, J, Schrodel, F, Vobwinkel, R, Bakosova, M & Soylemez, MT 2018, Robust Model Predictive Control Based on Stabilizing Parameter Space Calculus. in 2018 European Control Conference, ECC 2018., 8550618, 2018 European Control Conference, ECC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 206-212, 16th European Control Conference, ECC 2018, Limassol, Cyprus, 12/06/18. https://doi.org/10.23919/ECC.2018.8550618

Robust Model Predictive Control Based on Stabilizing Parameter Space Calculus. / Mutlu, Ilhan; Oravec, Juraj; Schrodel, Frank et al.
2018 European Control Conference, ECC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 206-212 8550618 (2018 European Control Conference, ECC 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Robust Model Predictive Control Based on Stabilizing Parameter Space Calculus

AU - Mutlu, Ilhan

AU - Oravec, Juraj

AU - Schrodel, Frank

AU - Vobwinkel, Rick

AU - Bakosova, Monika

AU - Soylemez, Mehmet Turan

PY - 2018/11/27

Y1 - 2018/11/27

N2 - The aim of this study is to introduce a novel approach for robust model predictive control (MPC) design based on stabilizing parameter spaces. In order to determine the stabilizing parameter regions, a Lyapunov equation based approach is proposed for nominal systems. In addition to the free controller parameters, it is also possible to determine boundaries of uncertain parameters in the present approach. The precomputed stability conditions on controller parameters are inserted to the MPC problem formulation as constraints. By this way, the stability of the closed-loop system is ensured. The proposed approach allows to design the nominal MPC, instead of the robust one. Using the predetermined constraints, the MPC is implemented to optimize the controller parameters over this stabilizing set. This paper introduces three particular control scenarios that tune the basic properties of the novel approach, e.g., runtime and computational effort. Two illustrative case studies are presented to demonstrate the efficiency of the proposed robust MPC strategy.

AB - The aim of this study is to introduce a novel approach for robust model predictive control (MPC) design based on stabilizing parameter spaces. In order to determine the stabilizing parameter regions, a Lyapunov equation based approach is proposed for nominal systems. In addition to the free controller parameters, it is also possible to determine boundaries of uncertain parameters in the present approach. The precomputed stability conditions on controller parameters are inserted to the MPC problem formulation as constraints. By this way, the stability of the closed-loop system is ensured. The proposed approach allows to design the nominal MPC, instead of the robust one. Using the predetermined constraints, the MPC is implemented to optimize the controller parameters over this stabilizing set. This paper introduces three particular control scenarios that tune the basic properties of the novel approach, e.g., runtime and computational effort. Two illustrative case studies are presented to demonstrate the efficiency of the proposed robust MPC strategy.

UR - http://www.scopus.com/inward/record.url?scp=85059814007&partnerID=8YFLogxK

U2 - 10.23919/ECC.2018.8550618

DO - 10.23919/ECC.2018.8550618

M3 - Conference contribution

AN - SCOPUS:85059814007

T3 - 2018 European Control Conference, ECC 2018

SP - 206

EP - 212

BT - 2018 European Control Conference, ECC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 16th European Control Conference, ECC 2018

Y2 - 12 June 2018 through 15 June 2018

ER -

Robust Model Predictive Control Based on Stabilizing Parameter Space Calculus

Abstract

Publication series

Conference

Bibliographical note

Funding

Access to Document

Other files and links

Fingerprint

Cite this