Control of Nonlinear Biological Systems by Non-minimal State Variable Feedback

摘要

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 CO_2 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 novelty of the present article is in the NMSS formulation and in 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.