@inproceedings{bd525ba7574645238433a47410f91094,
title = "Improving Industrial MPC Performance with Data-Driven Disturbance Modeling",
abstract = "Industrial model predictive control (MPC) usually assumes a step-like disturbance model, which is insufficient when there is model mismatch in the plant or high order disturbances. In this paper, we demonstrate that a disturbance model identified from close-loop data is desirable for dynamic matrix control (DMC). We introduce a subspace based method to obtain such a model. The method estimates Markov parameters of the disturbance model using closed-loop data along with known input-output model information in the DMC controller. Simulation results are given to compare the proposed approach with traditional DMC. {\textcopyright} 2011 IEEE.",
author = "Zhijie Sun and Yu Zhao and Qin, \{S. Joe\}",
year = "2011",
month = nov,
doi = "10.1109/CDC.2011.6161469",
language = "English",
isbn = "978-1-61284-800-6",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "IEEE",
pages = "1922--1927",
booktitle = "2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011",
address = "United States",
note = "2011 50th IEEE Conference on Decision and Control, and European Control Conference, CDC-ECC 2011 ; Conference date: 12-12-2011 Through 15-12-2011",
}