Taylor, C. James and Aerts, Jean-Marie (2014) Control of nonlinear biological systems by non–minimal state variable feedback. Statistics in Biosciences, 6 (2). pp. 290-313. ISSN 1867-1764
Full text not available from this repository.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.