Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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. стр. 1256-1261.Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
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 - Conference contribution
SP - 1256
EP - 1261
BT - 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017)
PB - IEEE Canada
T2 - 1st Annual IEEE Conference on Control Technology and Applications
Y2 - 27 August 2017 through 30 August 2017
ER -
ID: 32479520