Nonlinear estimation in a class of gene transcription process

摘要: In this work the Goodwin model applied to gene transcription is employed as a benchmark system for estimation purposes, considering two dynamic behaviors, monotone decreasing and sustained oscillations, each one under specific parameter's set. The preceding observability analysis of was done via linear differential-algebraic framework, where proved that fully observable from mRNA concentration measurements. Therefore class nonlinear observer which considers sigmoid functions output feedback, uncertainties, proposed sketch proof observer's convergence provided background Lyapunov theory, in order demonstrate asymptotic convergence. Numerical experiments are carrying out show performance methodology compared with standard Luenberger (Proportional) proportional sliding-mode (PSMO).