Nonlinear state estimation applied in a model of tequila production

This paper presents a comparative analysis of two nonlinear state estimators applied to the production process of tequila. The estimates consist of an extended Kalman filter (EKF) and a sliding mode observer (SMO), which are assessed to changes in process inputs and model parametric changes. The estimates are used to estimate the microbial growth and production of alcohol from the measurement of glucose concentration in the fermentation tank. The performance of the estimators is evaluated in terms of indicators of error.