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Randomized MPC with Model Uncertainties Based on LSCR Approach. / Kalmuk, Alexander; Tyushev, Kirill; Granichin, Oleg; DIng, Mingyue; Yuchi, Ming.

18th European Control Conference, ECC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 4307-4312 8796223.

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

Harvard

Kalmuk, A, Tyushev, K, Granichin, O, DIng, M & Yuchi, M 2019, Randomized MPC with Model Uncertainties Based on LSCR Approach. in 18th European Control Conference, ECC 2019., 8796223, Institute of Electrical and Electronics Engineers Inc., pp. 4307-4312, 18th European Control Conference, ECC 2019, Naples, Italy, 25/06/19. https://doi.org/10.23919/ECC.2019.8796223

APA

Kalmuk, A., Tyushev, K., Granichin, O., DIng, M., & Yuchi, M. (2019). Randomized MPC with Model Uncertainties Based on LSCR Approach. In 18th European Control Conference, ECC 2019 (pp. 4307-4312). [8796223] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ECC.2019.8796223

Vancouver

Kalmuk A, Tyushev K, Granichin O, DIng M, Yuchi M. Randomized MPC with Model Uncertainties Based on LSCR Approach. In 18th European Control Conference, ECC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 4307-4312. 8796223 https://doi.org/10.23919/ECC.2019.8796223

Author

Kalmuk, Alexander ; Tyushev, Kirill ; Granichin, Oleg ; DIng, Mingyue ; Yuchi, Ming. / Randomized MPC with Model Uncertainties Based on LSCR Approach. 18th European Control Conference, ECC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 4307-4312

BibTeX

@inproceedings{4169e33baa8849a7956122795c53d1fb,
title = "Randomized MPC with Model Uncertainties Based on LSCR Approach",
abstract = "This paper deals with a probabilistic approach for online system identification in MPC models with parameter uncertainties. The problem is to construct robust control in probabilistic sense for all possible values from some predefined set. In that case it is a good way to shrink the set over time in order to get better sets. The proposed algorithm is based on the modified LSCR (Leave-out Sign-dominant Correlation Regions) and applied to state space model used in MPC. In the paper we address a convergence of the algorithm over time, which required significant modifications comparing to our previous work. Practical part demonstrates in more detail with Matlab and CVX how to obtain smaller control for a nonminimal-phase second order plant with two unknown parameters and investigates convergence of confidence regions over time.",
keywords = "PREDICTIVE CONTROL, PARAMETERS",
author = "Alexander Kalmuk and Kirill Tyushev and Oleg Granichin and Mingyue DIng and Ming Yuchi",
year = "2019",
doi = "10.23919/ECC.2019.8796223",
language = "Английский",
isbn = "9783907144008",
pages = "4307--4312",
booktitle = "18th European Control Conference, ECC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "Соединенные Штаты Америки",
note = "null ; Conference date: 25-06-2019 Through 28-06-2019",

}

RIS

TY - GEN

T1 - Randomized MPC with Model Uncertainties Based on LSCR Approach

AU - Kalmuk, Alexander

AU - Tyushev, Kirill

AU - Granichin, Oleg

AU - DIng, Mingyue

AU - Yuchi, Ming

PY - 2019

Y1 - 2019

N2 - This paper deals with a probabilistic approach for online system identification in MPC models with parameter uncertainties. The problem is to construct robust control in probabilistic sense for all possible values from some predefined set. In that case it is a good way to shrink the set over time in order to get better sets. The proposed algorithm is based on the modified LSCR (Leave-out Sign-dominant Correlation Regions) and applied to state space model used in MPC. In the paper we address a convergence of the algorithm over time, which required significant modifications comparing to our previous work. Practical part demonstrates in more detail with Matlab and CVX how to obtain smaller control for a nonminimal-phase second order plant with two unknown parameters and investigates convergence of confidence regions over time.

AB - This paper deals with a probabilistic approach for online system identification in MPC models with parameter uncertainties. The problem is to construct robust control in probabilistic sense for all possible values from some predefined set. In that case it is a good way to shrink the set over time in order to get better sets. The proposed algorithm is based on the modified LSCR (Leave-out Sign-dominant Correlation Regions) and applied to state space model used in MPC. In the paper we address a convergence of the algorithm over time, which required significant modifications comparing to our previous work. Practical part demonstrates in more detail with Matlab and CVX how to obtain smaller control for a nonminimal-phase second order plant with two unknown parameters and investigates convergence of confidence regions over time.

KW - PREDICTIVE CONTROL

KW - PARAMETERS

UR - http://www.scopus.com/inward/record.url?scp=85071587716&partnerID=8YFLogxK

UR - http://www.mendeley.com/research/randomized-mpc-model-uncertainties-based-lscr-approach

U2 - 10.23919/ECC.2019.8796223

DO - 10.23919/ECC.2019.8796223

M3 - статья в сборнике материалов конференции

AN - SCOPUS:85071587716

SN - 9783907144008

SP - 4307

EP - 4312

BT - 18th European Control Conference, ECC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 25 June 2019 through 28 June 2019

ER -

ID: 47479859