An adjustable asymptotic observer for a fermentation process

In this paper we propose an adjustable asymptotic observer for a class of dynamical systems described by a distributed parameter model. This adjustable observer, whose theory is extended here to cover distributed parameter models, is based on the classical asymptotic observer. Moreover, such observer is provided with adjustable gains (that can be chosen by the user) without loosing the stability and robustness properties of the classical observer. We show through rigorous numerical simulations of a fermentation process carried out in a fluidized bed reactor, that the adjustable observer yields satisfactory estimates of the biomass and the product concentration by choosing a suitable gain set for the observer, in the face of parameter uncertainty