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Lead-acid batteries have been the most widely used energy storage units in stand-alone photovoltaic (PV) applications. To make a full use of those batteries and to improve their lifecycle, high performance charger is often required. The implementation of an advanced charger needs accurate information on the batteries internal parameters. In this work, we selected CIEMAT model because of its good performance to deal with the widest range of lead acid batteries. The performance evaluation of this model is based on the co-simulation LabVIEW/Multisim. With the intention of determining the impact of the charging process on batteries, the behaviour of different internal parameters of the batteries was simulated. During the charging mode, the value of the current must decrease when the batteries’ state of charge is close to be fully charged.

This paper deals with the development of dynamic models for the estimations of internal temperature and relative humidity of a greenhouse. Multilayers perceptron with 12 hidden neurons with a hyperbolic tangent as an activation function and which has been trained with Levenberg Marquardt (LM) algorithm. The data used to compute the simulation model were acquired in an experimental greenhouse using a sampling time interval of 10 seconds. The greenhouse is automated with several sensors and actuators that were connected to an acquisition and control system based on a personal computer. A comparison of measured and simulated data for both temperature and relative humidity under greenhouse showed that the elaborated models were able to identify and forecast inside greenhouse conditions reasonably well.

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<p>This paper deals with the design and implementation of the high order sliding mode controller to control temperature greenhouse. The control objective aims to ensure a favorable microclimate for the culture development and to minimize the production cost. We propose performing regulation for the greenhouse internal temperature based on the second order sliding mode technique known as Super Twisting Algorithm (STA). This technique is able to ensure robustness with respect to bounded external disturbances. A successful feasibility study of the proposed controller is applied to maintien a desired temperature level under an experimental greenhouse. The obtained results show promising performances despite changes of the external meteorological conditions.</p>