A control engineering approach for designing an optimized treatment plan for fibromyalgia

Sunil Deshpande, Naresh N. Nandola, Daniel Rivera, Jarred Younger

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

14 Scopus citations

Abstract

Control engineering offers a systematic and efficient means for optimizing the effectiveness of behavioral interventions. In this paper, we present an approach to develop dynamical models and subsequently, hybrid model predictive control schemes for assigning optimal dosages of naltrexone as treatment for a chronic pain condition known as fibromyalgia. We apply system identification techniques to develop models from daily diary reports completed by participants of a naltrexone intervention trial. The dynamic model serves as the basis for applying model predictive control as a decision algorithm for automated dosage selection of naltrexone in the face of the external disturbances. The categorical/discrete nature of the dosage assignment creates a need for hybrid model predictive control (HMPC) schemes. Simulation results that include conditions of significant plant-model mismatch demonstrate the performance and applicability of hybrid predictive control for optimized adaptive interventions for fibromyalgia treatment involving naltrexone.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4798-4803
Number of pages6
ISBN (Print)9781457700804
DOIs
StatePublished - 2011

Publication series

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

Keywords

  • fibromyalgia
  • hybrid model predictive control
  • optimized behavioral interventions
  • system identification

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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