Hybrid model predictive control for optimizing gestational weight gain behavioral interventions

Yuwen Dong, Daniel E. Rivera, Danielle Symons Downs, Jennifer Savage Williams, Diana M. Thomas, Linda Marie Collins

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

15 Citations (Scopus)

Abstract

Excessive gestational weight gain (GWG) represents a major public health issue. In this paper, we pursue a control engineering approach to the problem by applying model predictive control (MPC) algorithms to act as decision policies in the intervention for assigning optimal intervention dosages. The intervention components consist of education, behavioral modification and active learning. The categorical nature of the intervention dosage assignment problem dictates the need for hybrid model predictive control (HMPC) schemes, ultimately leading to improved outcomes. The goal is to design a controller that generates an intervention dosage sequence which improves a participant's healthy eating behavior and physical activity to better control GWG. An improved formulation of self-regulation is also presented through the use of Internal Model Control (IMC), allowing greater flexibility in describing self-regulatory behavior. Simulation results illustrate the basic workings of the model and demonstrate the benefits of hybrid predictive control for optimized GWG adaptive interventions.

Original languageEnglish (US)
Title of host publication2013 American Control Conference, ACC 2013
Pages1970-1975
Number of pages6
StatePublished - Sep 11 2013
Event2013 1st American Control Conference, ACC 2013 - Washington, DC, United States
Duration: Jun 17 2013Jun 19 2013

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2013 1st American Control Conference, ACC 2013
CountryUnited States
CityWashington, DC
Period6/17/136/19/13

Fingerprint

Model predictive control
Weight control
Public health
Education
Controllers

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Dong, Y., Rivera, D. E., Downs, D. S., Williams, J. S., Thomas, D. M., & Collins, L. M. (2013). Hybrid model predictive control for optimizing gestational weight gain behavioral interventions. In 2013 American Control Conference, ACC 2013 (pp. 1970-1975). [6580124] (Proceedings of the American Control Conference).
Dong, Yuwen ; Rivera, Daniel E. ; Downs, Danielle Symons ; Williams, Jennifer Savage ; Thomas, Diana M. ; Collins, Linda Marie. / Hybrid model predictive control for optimizing gestational weight gain behavioral interventions. 2013 American Control Conference, ACC 2013. 2013. pp. 1970-1975 (Proceedings of the American Control Conference).
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Dong, Y, Rivera, DE, Downs, DS, Williams, JS, Thomas, DM & Collins, LM 2013, Hybrid model predictive control for optimizing gestational weight gain behavioral interventions. in 2013 American Control Conference, ACC 2013., 6580124, Proceedings of the American Control Conference, pp. 1970-1975, 2013 1st American Control Conference, ACC 2013, Washington, DC, United States, 6/17/13.

Hybrid model predictive control for optimizing gestational weight gain behavioral interventions. / Dong, Yuwen; Rivera, Daniel E.; Downs, Danielle Symons; Williams, Jennifer Savage; Thomas, Diana M.; Collins, Linda Marie.

2013 American Control Conference, ACC 2013. 2013. p. 1970-1975 6580124 (Proceedings of the American Control Conference).

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

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Dong Y, Rivera DE, Downs DS, Williams JS, Thomas DM, Collins LM. Hybrid model predictive control for optimizing gestational weight gain behavioral interventions. In 2013 American Control Conference, ACC 2013. 2013. p. 1970-1975. 6580124. (Proceedings of the American Control Conference).