Héctor Antonio Botero Castro

A methodology for excitation systems identification

In this paper a methodology for parameter identification of the IEEE standard 421.5 (1992) excitation systems models is presented. The methodology applies parametric identification techniques to the linear dynamics of voltage regulator models. A pseudorandom binary sequence small signal is used in an offline generator test, that allows a reduction in the equipment test time, in comparison with step or frequency response methods. The remaining parameters for the saturation nonlinearities and exciter magnetic saturation are obtained by means of large signal excursion simple tests. The methodology is illustrated applying it to the IEEE ST1A model in simulation, and experimentally to a direct digital auto excited excitation system, represented with the IEEE ST4B model. Simulation and experimental results show the validity of the proposed methodology, with achieved successes percentages of 96% for simulation and 73% for experimentation

State and unknown input estimation in a CSTR using higher-order sliding mode observer

The aim of this paper is to design and analyze an observer based on high-order sliding mode to estimate states and unknown inputs in a continuous stirred tank reactor (CSTR). Additionally, the designed HOSM allows to reduce the chattering. The performance of HOSM observer is compared with a 1-order sliding mode observer.

Design and Implementation of a Sliding Mode Observer-Based Controller for a Mass-Spring System

This work presents the implementation of a sliding mode observer-based controller on a mass-spring experimental platform. The controller is based on the super-twisting algorithm and the observer is based on a high order sliding mode algorithm, to obtain continuous control signal. The simulations and practical results show a good performance of the complete structure.

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.

Sofware sensors design for monitoring of variables in reactors: A biological processes application

In this paper the design of state observers or software sensors for a biological process of ethanol production by S. cerevisiae is presented. Two observers (non-linear and sliding) were used for monitoring continuous fermentations. Their performances were evaluated using mathematical model for bioreactor (perfect knowledge of both model structure and parameters. The performance of the observers proposed in the present work showed a better adjustment that the classical Extended Luenberger observer.