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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>2014
<mark>Journal</mark>Statistics in Biosciences
Issue number2
Number of pages24
Pages (from-to)290-313
Publication StatusPublished
Early online date16/07/13
<mark>Original language</mark>English


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.