2026-05-24T09:37:25Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-1294642025-05-20T15:17:28Zoai_pmh:alloai_pmh:repo_items129464
https://hdl.handle.net/2286/R.I.27908
<p>Deshpande, Sunil, Nandola, Naresh N., Rivera, Daniel E., & Younger, Jarred W. (2014). Optimized treatment of fibromyalgia using system identification and hybrid model predictive control. CONTROL ENGINEERING PRACTICE, 33, 161-173. http://dx.doi.org/10.1016/j.conengprac.2014.09.011</p>
10.1016/j.conengprac.2014.09.011
0967-0661
http://rightsstatements.org/vocab/InC/1.0/
2014-12-01
30 pages
eng
Deshpande, Sunil
Nandola, Naresh
Rivera, Daniel
Younger, Jarred W.
Control Systems Engineering Laboratory
NOTICE: this is the author's version of a work that was accepted for publication. Changes may have been made to this work since it was submitted. A definitive version was subsequently published at http://dx.doi.org/10.1016/j.conengprac.2014.09.011
<p>The term adaptive intervention is used in behavioral health to describe individually tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.</p>
Text
Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control