TY - GEN
T1 - Improving Industrial MPC Performance with Data-Driven Disturbance Modeling
AU - SUN, Zhijie
AU - ZHAO, Yu
AU - QIN, S. Joe
PY - 2011/12
Y1 - 2011/12
N2 - 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. © 2011 IEEE.
AB - 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. © 2011 IEEE.
UR - http://www.scopus.com/inward/record.url?scp=84860697064&partnerID=8YFLogxK
U2 - 10.1109/CDC.2011.6161469
DO - 10.1109/CDC.2011.6161469
M3 - Conference paper (refereed)
SN - 9781612848006
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1922
EP - 1927
BT - 2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
PB - Institute of Electrical and Electronics Engineers
T2 - 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
Y2 - 12 December 2011 through 15 December 2011
ER -