@inproceedings{9e6b93072979470cb4b1440ea0da1b41,
title = "Hybrid model predictive control for sequential decision policies in adaptive behavioral interventions",
abstract = "Control engineering offers a systematic and efficient method to optimize the effectiveness of individually tailored treatment and prevention policies known as adaptive or 'just-in-time' behavioral interventions. The nature of these interventions requires assigning dosages at categorical levels, which has been addressed in prior work using Mixed Logical Dynamical (MLD)-based hybrid model predictive control (HMPC) schemes. However, certain requirements of adaptive behavioral interventions that involve sequential decision making have not been comprehensively explored in the literature. This paper presents an extension of the traditional MLD framework for HMPC by representing the requirements of sequential decision policies as mixed-integer linear constraints. This is accomplished with user-specified dosage sequence tables, manipulation of one input at a time, and a switching time strategy for assigning dosages at time intervals less frequent than the measurement sampling interval. A model developed for a gestational weight gain (GWG) intervention is used to illustrate the generation of these sequential decision policies and their effectiveness for implementing adaptive behavioral interventions involving multiple components.",
keywords = "Biomedical, Emerging control applications, Predictive control for linear systems",
author = "Yuwen Dong and Sunil Deshpande and Daniel Rivera and Downs, {Danielle S.} and Savage, {Jennifer S.}",
year = "2014",
doi = "10.1109/ACC.2014.6859462",
language = "English (US)",
isbn = "9781479932726",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4198--4203",
booktitle = "2014 American Control Conference, ACC 2014",
note = "2014 American Control Conference, ACC 2014 ; Conference date: 04-06-2014 Through 06-06-2014",
}