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Control of nonlinear biological systems by non–minimal state variable feedback

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Control of nonlinear biological systems by non–minimal state variable feedback. / Taylor, C. James; Aerts, Jean-Marie.
In: Statistics in Biosciences, Vol. 6, No. 2, 2014, p. 290-313.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Taylor, CJ & Aerts, J-M 2014, 'Control of nonlinear biological systems by non–minimal state variable feedback', Statistics in Biosciences, vol. 6, no. 2, pp. 290-313. https://doi.org/10.1007/s12561-013-9098-5

APA

Taylor, C. J., & Aerts, J-M. (2014). Control of nonlinear biological systems by non–minimal state variable feedback. Statistics in Biosciences, 6(2), 290-313. https://doi.org/10.1007/s12561-013-9098-5

Vancouver

Taylor CJ, Aerts J-M. Control of nonlinear biological systems by non–minimal state variable feedback. Statistics in Biosciences. 2014;6(2):290-313. Epub 2013 Jul 16. doi: 10.1007/s12561-013-9098-5

Author

Taylor, C. James ; Aerts, Jean-Marie. / Control of nonlinear biological systems by non–minimal state variable feedback. In: Statistics in Biosciences. 2014 ; Vol. 6, No. 2. pp. 290-313.

Bibtex

@article{fa923524ce974907b75730ed9a034a3a,
title = "Control of nonlinear biological systems by non–minimal state variable feedback",
abstract = "We contrast biostatistical methods for optimal treatment determination with optimal control methodology, originally developed in the engineering literature but now used more widely. We describe non-minimal state space (NMSS) control methods for biological systems, with a particular focus on the use of state-dependent parameter models to represent system nonlinearities. Three examples are considered, namely the control of (i) a nonlinear forced logistic function implemented with a time-delay; (ii) athletic horse heart rate with potential application for training improvement; and (iii) a physically--based simulation model for the uptake of CO2 by plant leaves in response to light intensity, with application to closed-environment grow cells. Although all three examples have been extensively studied in the literature, the novelties of the present article are in the NMSS formulation and the application of a recently developed state-dependent (nonlinear) control algorithm. In the case of the leaf photosynthesis simulation, however, the linear NMSS algorithm yields satisfactory results, illustrating the inherent robustness of feedback.",
keywords = "control, non-minimal state space, state-dependent parameter, forced logistic equation, stomatal conductance, horse heart rate",
author = "Taylor, {C. James} and Jean-Marie Aerts",
year = "2014",
doi = "10.1007/s12561-013-9098-5",
language = "English",
volume = "6",
pages = "290--313",
journal = "Statistics in Biosciences",
issn = "1867-1764",
publisher = "Springer New York",
number = "2",

}

RIS

TY - JOUR

T1 - Control of nonlinear biological systems by non–minimal state variable feedback

AU - Taylor, C. James

AU - Aerts, Jean-Marie

PY - 2014

Y1 - 2014

N2 - We contrast biostatistical methods for optimal treatment determination with optimal control methodology, originally developed in the engineering literature but now used more widely. We describe non-minimal state space (NMSS) control methods for biological systems, with a particular focus on the use of state-dependent parameter models to represent system nonlinearities. Three examples are considered, namely the control of (i) a nonlinear forced logistic function implemented with a time-delay; (ii) athletic horse heart rate with potential application for training improvement; and (iii) a physically--based simulation model for the uptake of CO2 by plant leaves in response to light intensity, with application to closed-environment grow cells. Although all three examples have been extensively studied in the literature, the novelties of the present article are in the NMSS formulation and the application of a recently developed state-dependent (nonlinear) control algorithm. In the case of the leaf photosynthesis simulation, however, the linear NMSS algorithm yields satisfactory results, illustrating the inherent robustness of feedback.

AB - We contrast biostatistical methods for optimal treatment determination with optimal control methodology, originally developed in the engineering literature but now used more widely. We describe non-minimal state space (NMSS) control methods for biological systems, with a particular focus on the use of state-dependent parameter models to represent system nonlinearities. Three examples are considered, namely the control of (i) a nonlinear forced logistic function implemented with a time-delay; (ii) athletic horse heart rate with potential application for training improvement; and (iii) a physically--based simulation model for the uptake of CO2 by plant leaves in response to light intensity, with application to closed-environment grow cells. Although all three examples have been extensively studied in the literature, the novelties of the present article are in the NMSS formulation and the application of a recently developed state-dependent (nonlinear) control algorithm. In the case of the leaf photosynthesis simulation, however, the linear NMSS algorithm yields satisfactory results, illustrating the inherent robustness of feedback.

KW - control

KW - non-minimal state space

KW - state-dependent parameter

KW - forced logistic equation

KW - stomatal conductance

KW - horse heart rate

U2 - 10.1007/s12561-013-9098-5

DO - 10.1007/s12561-013-9098-5

M3 - Journal article

VL - 6

SP - 290

EP - 313

JO - Statistics in Biosciences

JF - Statistics in Biosciences

SN - 1867-1764

IS - 2

ER -