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Online Parameter Estimation for MPC Model Uncertainties Based on LSCR Approach. / Kalmuk, Alexander; Tyushev, Kirill; Granichin, Oleg; Yuchi, Ming.

2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017). IEEE Canada, 2017. p. 1256-1261.

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

Harvard

Kalmuk, A, Tyushev, K, Granichin, O & Yuchi, M 2017, Online Parameter Estimation for MPC Model Uncertainties Based on LSCR Approach. in 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017). IEEE Canada, pp. 1256-1261, 1st Annual IEEE Conference on Control Technology and Applications, United States, 27/08/17.

APA

Kalmuk, A., Tyushev, K., Granichin, O., & Yuchi, M. (2017). Online Parameter Estimation for MPC Model Uncertainties Based on LSCR Approach. In 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017) (pp. 1256-1261). IEEE Canada.

Vancouver

Kalmuk A, Tyushev K, Granichin O, Yuchi M. Online Parameter Estimation for MPC Model Uncertainties Based on LSCR Approach. In 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017). IEEE Canada. 2017. p. 1256-1261

Author

Kalmuk, Alexander ; Tyushev, Kirill ; Granichin, Oleg ; Yuchi, Ming. / Online Parameter Estimation for MPC Model Uncertainties Based on LSCR Approach. 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017). IEEE Canada, 2017. pp. 1256-1261

BibTeX

@inproceedings{c9c3885dacca4f2894cd141f1aba5513,
title = "Online Parameter Estimation for MPC Model Uncertainties Based on LSCR Approach",
abstract = "The paper discusses a novel probabilistic approach for online parameter estimation of the predictor model used in an MPC (Model Predictive Control) setting in the presence of model uncertainties and external disturbances. Model uncertainty makes it hard to compute an optimal control in general case, because it is needed to take into account all possible values of model parameters. Therefore, it is a good way for optimisation to shrink a set of possible model parameters. The proposed method iteratively estimates model parameters using randomized control strategy and algorithm based on LSCR (Leave-out Sign-dominant Correlation Regions) and computes a new control for the estimated parameters using robust MPC. The theoretical results are demonstrated via a model simulation example with two unknown parameters.",
keywords = "PREDICTIVE CONTROL",
author = "Alexander Kalmuk and Kirill Tyushev and Oleg Granichin and Ming Yuchi",
year = "2017",
language = "Английский",
pages = "1256--1261",
booktitle = "2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017)",
publisher = "IEEE Canada",
address = "Канада",
note = "null ; Conference date: 27-08-2017 Through 30-08-2017",

}

RIS

TY - GEN

T1 - Online Parameter Estimation for MPC Model Uncertainties Based on LSCR Approach

AU - Kalmuk, Alexander

AU - Tyushev, Kirill

AU - Granichin, Oleg

AU - Yuchi, Ming

PY - 2017

Y1 - 2017

N2 - The paper discusses a novel probabilistic approach for online parameter estimation of the predictor model used in an MPC (Model Predictive Control) setting in the presence of model uncertainties and external disturbances. Model uncertainty makes it hard to compute an optimal control in general case, because it is needed to take into account all possible values of model parameters. Therefore, it is a good way for optimisation to shrink a set of possible model parameters. The proposed method iteratively estimates model parameters using randomized control strategy and algorithm based on LSCR (Leave-out Sign-dominant Correlation Regions) and computes a new control for the estimated parameters using robust MPC. The theoretical results are demonstrated via a model simulation example with two unknown parameters.

AB - The paper discusses a novel probabilistic approach for online parameter estimation of the predictor model used in an MPC (Model Predictive Control) setting in the presence of model uncertainties and external disturbances. Model uncertainty makes it hard to compute an optimal control in general case, because it is needed to take into account all possible values of model parameters. Therefore, it is a good way for optimisation to shrink a set of possible model parameters. The proposed method iteratively estimates model parameters using randomized control strategy and algorithm based on LSCR (Leave-out Sign-dominant Correlation Regions) and computes a new control for the estimated parameters using robust MPC. The theoretical results are demonstrated via a model simulation example with two unknown parameters.

KW - PREDICTIVE CONTROL

M3 - статья в сборнике материалов конференции

SP - 1256

EP - 1261

BT - 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017)

PB - IEEE Canada

Y2 - 27 August 2017 through 30 August 2017

ER -

ID: 32479520