TY - GEN
T1 - Data-driven control of infinite dimensional systems
T2 - 2021 American Control Conference, ACC 2021
AU - Kergus, Pauline
PY - 2021/5/25
Y1 - 2021/5/25
N2 - Controlling infinite dimensional models remains a challenging task for many practitioners since they are not suitable for traditional control design techniques or will result in a high-order controller too complex for implementation. Therefore, the model or the controller need to be reduced to an acceptable dimension, which is time-consuming, requires some expertise and may introduce numerical error. This paper tackles the control of such a system, namely a continuous crystallizer, and compares two different data-driven strategies: the first one is a structured robust technique while the other one, called L-DDC, is based on the Loewner interpolatory framework. Model/Controller reduction, Stability of linear systems, Control applications.
AB - Controlling infinite dimensional models remains a challenging task for many practitioners since they are not suitable for traditional control design techniques or will result in a high-order controller too complex for implementation. Therefore, the model or the controller need to be reduced to an acceptable dimension, which is time-consuming, requires some expertise and may introduce numerical error. This paper tackles the control of such a system, namely a continuous crystallizer, and compares two different data-driven strategies: the first one is a structured robust technique while the other one, called L-DDC, is based on the Loewner interpolatory framework. Model/Controller reduction, Stability of linear systems, Control applications.
U2 - 10.23919/ACC50511.2021.9483117
DO - 10.23919/ACC50511.2021.9483117
M3 - Paper in conference proceeding
AN - SCOPUS:85111912382
T3 - Proceedings of the American Control Conference
SP - 1438
EP - 1443
BT - 2021 American Control Conference, ACC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 May 2021 through 28 May 2021
ER -