In this work, simple tuning rules for feedforward compensators were applied to design a control strategy to regulate the inside air temperature of a greenhouse during daytime by means of a natural ventilation system. The developed control strategy is based on a PI (Proportional-Integral) controller combined with feedforward compensators to improve the performance against measurable external disturbances such as outside air temperature, solar radiation, and wind velocity. Since the greenhouse process dynamics is very complex and physical non-linear models are mathematically complicated, a system identification methodology was proposed to obtain simpler models (high-order polynomial and low-order transfer functions). Thus, an easier procedure was completed to tune the PI controller parameters and to obtain the feedforward compensators expressions by following a series of modern and simple tuning rules. Simulations with real data were executed to compare the control performance of a PI controller with or without the addition of feedforward compensators. Moreover, real tests for the developed control strategy were carried out in an experimental greenhouse. Results demonstrate an enhanced control performance with the presence of the feedforward compensators under different weather conditions.
Production of microalgae is one of the emerging biotechnological processes due to its potential applications to produce high value-added compounds. In photobioreactors for microalgae production, the biomass concentration is a desirable variable to be measured on-line to optimize the yield of the systems. However, biomass concentration can hardly be monitored in real time. There are few expensive commercial sensors that in fact provide uncertain measurements. State estimators, also known as software sensors, are algorithms designed to estimate unmeasured (or non-easily measurable) variables of a process. In this work, a state estimator using the extended Kalman filter algorithm is developed to estimate biomass concentration for an outdoor industrial raceway photobioreactor. The state estimator is based on a dynamic model for microalgae production specifically designed for this type of photobioreactor. Results demonstrate that, despite the complex non-linear dynamics that characterise this kind of bioprocess, a state estimator can provide a relatively accurate estimation of the biomass concentration. Furthermore, a state estimator could be used to optimize the operation of industrial photobioreactors by utilizing the estimated biomass concentration for automatic control of the process.
Este artículo presenta la aplicación de un esquema de control selectivo de temperatura y humedad para invernaderos solares chinos, que son los más utilizados en las provincias del norte de China. En primer lugar, para controlar la temperatura, se propone un controlador PI con un enfoque basado en eventos. Tras la evaluación de varios valores de la banda de ocurrencia de eventos, se obtiene una solución que permite reducir en un 43,8%el número de aperturas y cierres de las ventanas del invernadero, mientras que el error de temperatura se incrementa sólo en un 1,13 %. En segundo lugar, se ha diseñado un controlador para la humedad relativa y otro para la humedad absoluta. Los resultados muestran que el control de humedad relativa funciona adecuadamente cuando la consigna no es demasiado elevada. Sin embargo, la acción de control se deteriora cuando la consigna es superior al 70 %. En comparación, el control de humedad absoluta permite regular la humedad para referencias de cualquier valor, pero la precisión de control es menor. Finalmente, mediante un estudio en simulación, se demuestra la efectividad de la estrategia de control selectivo de temperatura con un esquema que da prioridad para controlar la humedad cuando ésta alcanza límites no deseados. Esta estrategia de control consigue mantener la humedad relativa por debajo del 80% mientras que controla la temperatura en la consigna establecida, evitando así que la alta humedad dañe al cultivo.
Este trabajo presenta el diseño de técnicas de control climático de invernaderos a través de la ventilación natural, explotando las perturbaciones medibles y teniendo en cuenta las restricciones de operación. Un invernadero constituye una planta ideal para el crecimiento de cultivos, ya que es un entorno cerrado donde las variables del clima y la fertirrigación pueden ser controladas mediante actuadores climáticos. Se describe el modelo no lineal utilizado para simular el comportamiento del invernadero usando datos de una instalación real, la calibración del mismo y el desarrollo y comparación de técnicas de control que hacen uso de modelos linealizados en torno a puntos de operación y del modelo no lineal completo (linealización por realimentación).
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