A robust MPC approach to the design of behavioural treatments

Korkut Bekiroglu, Constantino Manuel Lagoa, S. A. Murphy, Stephanie Trea Lanza

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

2 Citations (Scopus)

Abstract

The objective of this paper is to provide some initial results on the application of control tools to the problem treatment design. Human behavior and reaction to treatment is complex and dependent on many unmeasurable external stimuli. Therefore, to the best of our knowledge, it cannot be described by simple models. Hence, one of the main messages in this paper is that, to design a treatment (controller) one cannot rely on exact models. More precisely, to be able to design effective treatments, we propose to use "simple" uncertain affine models whose response "covers" the most probable subject responses. So, we propose a simple model that contains two different types of uncertainties: one aimed at uncertainty of the dynamics and another aimed at approximating external perturbations that patients face in their daily life. With this model at hand, we design a robust model predictive controller, where one relies on the special structure of the uncertainty to develop efficient optimization algorithms.

Original languageEnglish (US)
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3505-3510
Number of pages6
ISBN (Print)9781467357173
DOIs
StatePublished - Jan 1 2013
Event52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: Dec 10 2013Dec 13 2013

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other52nd IEEE Conference on Decision and Control, CDC 2013
CountryItaly
CityFlorence
Period12/10/1312/13/13

Fingerprint

Uncertainty
Model
Controller
Controllers
Human Behavior
Probable
Design
Optimization Algorithm
Efficient Algorithms
Cover
Perturbation
Dependent
Life
Knowledge

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Cite this

Bekiroglu, K., Lagoa, C. M., Murphy, S. A., & Lanza, S. T. (2013). A robust MPC approach to the design of behavioural treatments. In 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013 (pp. 3505-3510). [6760421] (Proceedings of the IEEE Conference on Decision and Control). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2013.6760421
Bekiroglu, Korkut ; Lagoa, Constantino Manuel ; Murphy, S. A. ; Lanza, Stephanie Trea. / A robust MPC approach to the design of behavioural treatments. 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013. Institute of Electrical and Electronics Engineers Inc., 2013. pp. 3505-3510 (Proceedings of the IEEE Conference on Decision and Control).
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Bekiroglu, K, Lagoa, CM, Murphy, SA & Lanza, ST 2013, A robust MPC approach to the design of behavioural treatments. in 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013., 6760421, Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc., pp. 3505-3510, 52nd IEEE Conference on Decision and Control, CDC 2013, Florence, Italy, 12/10/13. https://doi.org/10.1109/CDC.2013.6760421

A robust MPC approach to the design of behavioural treatments. / Bekiroglu, Korkut; Lagoa, Constantino Manuel; Murphy, S. A.; Lanza, Stephanie Trea.

2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013. Institute of Electrical and Electronics Engineers Inc., 2013. p. 3505-3510 6760421 (Proceedings of the IEEE Conference on Decision and Control).

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

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Bekiroglu K, Lagoa CM, Murphy SA, Lanza ST. A robust MPC approach to the design of behavioural treatments. In 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013. Institute of Electrical and Electronics Engineers Inc. 2013. p. 3505-3510. 6760421. (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2013.6760421