On Robustness against Temperature Changes of Adaptive Estimator for CAM Plants

Totoki, Yusuke; Suemitsu, Haruo; Matsuo, Takami

doi:10.5687/sss.2010.7

Cited by 2 publications

(1 citation statement)

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“…Finally, we design a first-order adaptive observer for online estimation of the nonlinear function in the dynamics of the tonoplast order using the output signal as the algebraic estimate of . The use of the algebraic estimate allows us to improve the convergence speed of the estimator in the previous paper [9,11,12]. The observer can attenuate the estimation error of the tonoplast order against the measurement noises.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Estimation of Biological Rhythm in Crassulacean Acid Metabolism with Critical Manifold

Matsuo

Totoki

Suemitsu

2013

ISRN Applied Mathematics

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The mechanism of endogenous circadian photosynthesis oscillations of plants performing crassulacean acid metabolism (CAM) is investigated in terms of a nonlinear theoretical model. Blasius et al. used throughout continuous time differential equations which adequately reflect the CAM dynamics. The model shows regular endogenous limit cycle oscillations that are stable for a wide range of temperatures in a manner that complies well with experimental data. In this paper, we pay attention to the approximation of the fast modes of the CAM dynamics. Using the zero-epsilon approximation of the slow manifold, we derive the critical manifold that is defined by two algebraic nonlinear equations. The critical manifold allows us to give the algebraic estimate of the order of the tonoplast membrane. The dynamic equation of the order of the tonoplast membrane includes the nonlinear function that gives the equilibrium value of the lipid order of tonoplast functions as a hysteresis switch. We identify the nonlinear function with the measurement signals. Using the basis function expansion of the nonlinear and the critical manifold, we propose an adaptive observer to estimate the tonoplast order and the nonlinear function.

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Section: Introductionmentioning

confidence: 99%