Hybrid model predictive control for sequential decision policies in adaptive behavioral interventions

Yuwen Dong, Sunil Deshpande, Daniel Rivera, Danielle S. Downs, Jennifer S. Savage

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

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.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4198-4203
Number of pages6
ISBN (Print)9781479932726
DOIs
StatePublished - 2014
Event2014 American Control Conference, ACC 2014 - Portland, OR, United States
Duration: Jun 4 2014Jun 6 2014

Other

Other2014 American Control Conference, ACC 2014
CountryUnited States
CityPortland, OR
Period6/4/146/6/14

Fingerprint

Model predictive control
Decision making
Sampling

Keywords

  • Biomedical
  • Emerging control applications
  • Predictive control for linear systems

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Dong, Y., Deshpande, S., Rivera, D., Downs, D. S., & Savage, J. S. (2014). Hybrid model predictive control for sequential decision policies in adaptive behavioral interventions. In Proceedings of the American Control Conference (pp. 4198-4203). [6859462] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2014.6859462

Hybrid model predictive control for sequential decision policies in adaptive behavioral interventions. / Dong, Yuwen; Deshpande, Sunil; Rivera, Daniel; Downs, Danielle S.; Savage, Jennifer S.

Proceedings of the American Control Conference. Institute of Electrical and Electronics Engineers Inc., 2014. p. 4198-4203 6859462.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Dong, Y, Deshpande, S, Rivera, D, Downs, DS & Savage, JS 2014, Hybrid model predictive control for sequential decision policies in adaptive behavioral interventions. in Proceedings of the American Control Conference., 6859462, Institute of Electrical and Electronics Engineers Inc., pp. 4198-4203, 2014 American Control Conference, ACC 2014, Portland, OR, United States, 6/4/14. https://doi.org/10.1109/ACC.2014.6859462
Dong Y, Deshpande S, Rivera D, Downs DS, Savage JS. Hybrid model predictive control for sequential decision policies in adaptive behavioral interventions. In Proceedings of the American Control Conference. Institute of Electrical and Electronics Engineers Inc. 2014. p. 4198-4203. 6859462 https://doi.org/10.1109/ACC.2014.6859462
Dong, Yuwen ; Deshpande, Sunil ; Rivera, Daniel ; Downs, Danielle S. ; Savage, Jennifer S. / Hybrid model predictive control for sequential decision policies in adaptive behavioral interventions. Proceedings of the American Control Conference. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 4198-4203
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