Adaptive Asymptotic Tracking for a Class of Uncertain Switched Positive Compartmental Models with Application to Anesthesia

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Abstract

This article addresses and solves the adaptive asymptotic tracking for a class of uncertain switched positive linear dynamics (also known in the literature as compartmental models) subject to dwell-time constraints. Compared to the state-of-the-art, the innovative feature of this method is to attain for the first time asymptotic set-point tracking, while guaranteeing non-negativity of the systems states. To achieve asymptotic tracking, an interpolated Lyapunov function is adopted, which is nonincreasing at the switching instants and decreasing in two consecutive switching instants. Such Lyapunov function results in a novel adaptive law with time-varying adaptive gains, as opposed to state-of-the-art laws with fixed positive adaptive gains. The developed design is applicable to classes of compartmental systems compatible with those proposed in the literature: an example involving the infusion of anesthesia is conducted to show that the proposed method can achieve better performance than existing methods.

Original languageEnglish
Pages (from-to)4936-4942
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume51
Issue number8
DOIs
Publication statusPublished - 2021

Bibliographical note

Accepted Author Manuscript

Keywords

  • Adaptive asymptotic control
  • compartmental systems
  • dwell time
  • switched positive linear dynamics

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