Adaptive control for nonlinear compartmental dynamical systems with applications to clinical pharmacology

Haddad, Wassim M.; Hayakawa, Tomohisa; Bailey, James M.

doi:10.1016/j.sysconle.2005.05.002

Cited by 43 publications

(22 citation statements)

References 22 publications

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“…In this research [32,48], we extend the results to nonnegative and compartmental dynamical systems with applications to the specific problem of automated anesthesia. Specifically, we develop an output feedback neural network adaptive controller that operates over.a tapped delay line of available input and output measurements.…”

Section: Neural Network Adaptive Control For Intensive Care Unit Sedamentioning

confidence: 91%

A Network Thermodynamic Framework for the Analysis and Control Design of Large-Scale Dynamical Systems

Haddad¹,

Balzano²

2006

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Section: Neural Network Adaptive Control For Intensive Care Unit Sedamentioning

confidence: 91%

A Network Thermodynamic Framework for the Analysis and Control Design of Large-Scale Dynamical Systems

Haddad¹,

Balzano²

2006

Self Cite

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“…Furthermore, V (x, K , , ) is radially unbounded. Now, letting x(t), t 0, denote the solution to (17) and using (14)- (16), it follows that the Lyapunov…”

Section: Proofmentioning

confidence: 99%

“…Their usage includes demographic, epidemic [7], ecological [10], economic [11], telecommunications [12], transportation, power, and large-scale systems [13].In a recent series of papers [14-16], a direct adaptive control framework for linear and nonlinear nonnegative and compartmental systems was developed. The framework in [14][15][16] is Lyapunov based and guarantees partial asymptotic set-point regulation, that is, asymptotic set-point stability with respect to the closed-loop system states associated with the plant. In addition, the adaptive controllers in [14-16] guarantee that the physical system states remain in the nonnegative orthant of the state space.…”

mentioning

confidence: 99%

Adaptive disturbance rejection control for compartmental systems with application to intraoperative anesthesia influenced by hemorrhage and hemodilution effects

Volyanskyy¹,

Haddad²,

Bailey³

2008

Adaptive Control & Signal

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Compartmental system models involve dynamic states whose values are nonnegative. These models are widespread in biological and physiological sciences and play a key role in understanding these processes. In this paper, we develop a direct adaptive disturbance rejection control framework for compartmental dynamical systems with exogenous bounded disturbances. The proposed framework is Lyapunov based and guarantees partial asymptotic stability of the closed-loop system, that is, asymptotic stability with respect to part of the closed-loop system states associated with the plant dynamics. The remainder of the states associated with the adaptive controller gains are shown to be Lyapunov stable. In the case of bounded energy L 2 disturbances, the proposed approach guarantees a nonexpansivity constraint on the closed-loop input-output map between the plant disturbances and performance variables. Finally, a numerical example involving the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for surgery in the face of continuing hemorrhage and hemodilution is provided.

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“…The main advantage of a single update before the maintenance phase begins, as opposed to continuous adaptation (see e.g. [1]), is that unmeasured disturbances during the maintenance phase do not result in poor performance or even instability due to drifting parameters. Focus does not lie on the controller tuning per se, but rather on what can be gained by the proposed individualization.…”

Section: Introductionmentioning

confidence: 99%

Individualized PID control of depth of anesthesia based on patient model identification during the induction phase of anesthesia

Soltesz

Hahn

Dumont

et al. 2011

IEEE Conference on Decision and Control and European Control Conference

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Adaptive control for nonlinear compartmental dynamical systems with applications to clinical pharmacology

Cited by 43 publications

References 22 publications

A Network Thermodynamic Framework for the Analysis and Control Design of Large-Scale Dynamical Systems

A Network Thermodynamic Framework for the Analysis and Control Design of Large-Scale Dynamical Systems

Adaptive disturbance rejection control for compartmental systems with application to intraoperative anesthesia influenced by hemorrhage and hemodilution effects

Individualized PID control of depth of anesthesia based on patient model identification during the induction phase of anesthesia

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