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Dealing with observational data in control

Lookup NU author(s): Dr Quentin Clairon, Professor Robin Henderson



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2018 The Authors 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.

Publication metadata

Author(s): Wilson ED, Clairon Q, Henderson R, Taylor CJ

Publication type: Article

Publication status: Published

Journal: Annual Reviews in Control

Year: 2018

Volume: 46

Pages: 94-106

Print publication date: 01/06/2018

Online publication date: 29/05/2018

Acceptance date: 21/05/2018

Date deposited: 12/06/2018

ISSN (print): 1367-5788

ISSN (electronic): 1872-9088

Publisher: Pergamon Press


DOI: 10.1016/j.arcontrol.2018.05.001


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