Optimized treatment of fibromyalgia using system identification and hybrid model predictive control

被引:18
|
作者
Deshpande, Sunil [1 ]
Nandola, Naresh N. [1 ]
Rivera, Daniel E. [1 ]
Younger, Jarred W. [2 ]
机构
[1] Arizona State Univ, Sch Engn Matter Transport & Energy, Control Syst Engn Lab, Tempe, AZ 85287 USA
[2] Univ Alabama Birmingham, Dept Psychol, Birmingham, AL 35294 USA
基金
美国国家卫生研究院;
关键词
Optimized adaptive behavioral interventions; Fibromyalgia; System identification; Hybrid model predictive control; Biomedical applications; LOW-DOSE NALTREXONE; ADAPTIVE INTERVENTIONS; CRITERIA; DESIGN;
D O I
10.1016/j.conengprac.2014.09.011
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:161 / 173
页数:13
相关论文
共 50 条
  • [21] Control of Exhaust Recompression HCCI using Hybrid Model Predictive Control
    Widd, Anders
    Liao, Hsien-Hsin
    Gerdes, J. Christian
    Tunestal, Per
    Johansson, Rolf
    2011 AMERICAN CONTROL CONFERENCE, 2011, : 420 - 425
  • [22] Hybrid model-predictive-control for mechanical system with backlash
    Dong, Ling-Xun
    Dou, Li-Hua
    Chen, Jie
    Xia, Yuan-Qing
    Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2009, 26 (12): : 1378 - 1382
  • [23] Predictive control for mechanical system with backlash based on hybrid model
    Dou Lihua
    Dong Lingxun
    Chen Jie
    Xia Yuanqing
    JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 2009, 20 (06) : 1301 - 1308
  • [25] Suboptimal target control for hybrid automata using model predictive control
    Pang, Yan
    Spathopoulos, Michael P.
    Xia, Hao
    NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS, 2006, 65 (06) : 1211 - 1230
  • [26] Continuous nonlinear model predictive control of a hybrid water system
    Nederkoorn, Eelco
    Schuurmans, Jan
    Grispen, Joep
    Schuurmans, Wytze
    JOURNAL OF HYDROINFORMATICS, 2013, 15 (02) : 246 - 257
  • [27] A Control Engineering Approach for Designing an Optimized Treatment Plan for Fibromyalgia
    Deshpande, Sunil
    Nandola, Naresh N.
    Rivera, Daniel E.
    Younger, Jarred
    2011 AMERICAN CONTROL CONFERENCE, 2011, : 4798 - 4803
  • [28] Load frequency control of an isolated microgrid using optimized model predictive control by GA
    Tudu, Ayan Kumar
    Naguru, Nageswarappa
    Dey, Sunita Halder Nee
    Paul, Subrata
    ELECTRICAL ENGINEERING, 2024, 106 (04) : 4171 - 4183
  • [29] Model Predictive Control for the Self-optimized Operation in Wastewater Treatment Plants
    Francisco, Mario
    Skogestad, Sigurd
    Vega, Pastora
    12TH INTERNATIONAL SYMPOSIUM ON PROCESS SYSTEMS ENGINEERING (PSE) AND 25TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING (ESCAPE), PT B, 2015, 37 : 1703 - 1708
  • [30] Optimized Current Control of Vienna Rectifier Using Finite Control Set Model Predictive Control
    Izadinia, Ali R.
    Karshenas, Hamid R.
    2016 7TH POWER ELECTRONICS AND DRIVE SYSTEMS & TECHNOLOGIES CONFERENCE (PEDSTC), 2016, : 596 - 601