Final published version
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Dealing with observational data in control
AU - Wilson, Emma D.
AU - Clairon, Q.
AU - Henderson, R.
AU - Taylor, C. James
PY - 2018
Y1 - 2018
N2 - There is growing interest in the use of control theory for interdisciplinary applications, where data may be sparse or missing, be non-uniformly sampled, have greater uncertainty, and where there is no opportunity to collect repeat measurements. In such applications, problems posed by observational data and the issue of missing or irregular data need to be considered. We present a review on dealing with observational, missing and irregular data for control applications. This considers the following issues: i) how to identify a system model from observational data subject to missing measurements, ii) how to determine control inputs when output data includes missing measurements, and iii) how to ensure stability when future update times may be missed. Dealing with observational data and missing measurements is a key problem within the statistics literature, so we introduce statistical methods for dealing with this type of data. We aim to enable the integration of well-developed statistical methods of dealing with missing data into control theory. An example problem of using anticoagulants to control the blood clotting speed of patients is used throughout the paper.
AB - There is growing interest in the use of control theory for interdisciplinary applications, where data may be sparse or missing, be non-uniformly sampled, have greater uncertainty, and where there is no opportunity to collect repeat measurements. In such applications, problems posed by observational data and the issue of missing or irregular data need to be considered. We present a review on dealing with observational, missing and irregular data for control applications. This considers the following issues: i) how to identify a system model from observational data subject to missing measurements, ii) how to determine control inputs when output data includes missing measurements, and iii) how to ensure stability when future update times may be missed. Dealing with observational data and missing measurements is a key problem within the statistics literature, so we introduce statistical methods for dealing with this type of data. We aim to enable the integration of well-developed statistical methods of dealing with missing data into control theory. An example problem of using anticoagulants to control the blood clotting speed of patients is used throughout the paper.
KW - interdisciplinarity
KW - observational data
KW - causation
KW - system identification
KW - missing measurements
KW - missing data
KW - longitudinal data
KW - periodic control
KW - irregular control
KW - event driven control
U2 - 10.1016/j.arcontrol.2018.05.001
DO - 10.1016/j.arcontrol.2018.05.001
M3 - Journal article
VL - 46
SP - 94
EP - 106
JO - Annual Reviews in Control
JF - Annual Reviews in Control
SN - 1367-5788
ER -