Particle filter-based robust adaptive control for closed-loop administration of sodium nitroprusside

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    Abstract

    Automatic closed-loop administration of medicinal drugs has been the subject of intense research for decades due to its undisputed potential benefits in terms of cost savings and improved patient outcomes. However, concerns still exist about the ultimate safety of engineered feedback controllers. Manual methods remain dominant in clinical practice. In this context, we present a novel feedback control architecture, which combines multiple robust controllers with a particle filter-based method for real-time tracking of a patients dose-response characteristic. The proposed method is applied to the case of the drug sodium nitroprusside, a vasodepressor used in the treatment of acute hypertension in intensive care and surgery, which is modelled as having a linear-time-varying dose-response characteristic. Our design takes into account the uncertainty in the patient response parameters, as well as potential nonzero-mean disturbances in the baseline arterial pressure and several possible time trends in the variation of the dose-response model. The performance and safety of the new approach are evaluated through an extensive computational simulation campaign. The results show that the proposed system can achieve adequate and safe feedback control of mean arterial pressure, thus validating our analysis and design. Our findings also highlight the fundamental - and possibly clinically overlooked - role of system excitation in ensuring that successful simultaneous identification and control of time-varying drug administration systems can be achieved.
    Original languageEnglish
    Pages (from-to)19pp
    JournalJournal of Computational Surgery
    Volume1
    Issue number8
    DOIs
    Publication statusPublished - 2014

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