Constrained discrete model predictive control of a greenhouse system temperature

In this paper, a constrained discete model predictive control (CDMPC) strategy for a greenhouse inside temperature is presented. To describe the dynamics of our system’s inside temperature, an experimental greenhouse prototype is engaged. For the mathematical modeling, a state space form which fits properly the acquired data of the greenhouse temperature dynamics is identified using the subspace system identification (N4sid) algorithm. The obtained model is used in order to develop the CDMPC starategy which role is to select the best control moves based on an optimization procedure under the constraints on the control notion. For efficient evaluation of the proposed control approach Matlab/Simulink and Yalmip optimization toolbox are used for algorithm and blocks implementation. The simulation results confirm the accuracy of the controller that garantees both the control and the reference tracking objectives

Constrained Discrete Model Predictive Control of a Greenhouse Relative Humidity

In this paper, we present a Constrained Discete Model Predictive Control (CDMPC) strategy application for relative humidity control. In this sense, and for our system inside humidity dynamics description, a green-house prototype is engaged and a state space form which fits properly a set of collected data of the greenhouse humidity dynamics is presented as mathematical model. This latest is used for the CDMPC starategy application, which purpose is to select the best control moves based on an optimization procedure regarding the constraints on the control. By the means of Matlab/ Simulink and Yalmip toolbox algorithms, numerical simulations were held to proove the effectiveness of the controller, garanteeing both the constraints feasibility and system stability

An intelligent lead-acid battery closed-loop charger using a combined fuzzy controller for PV applications

This paper presents the modeling of an intelligent combined MPPT and Lead-Acid battery charger controller for standalone solar photovoltaic systems. It involves the control of a DC/DC buck converter through a control unit, which contains two cascaded fuzzy logic controllers (FLC), that adjusts the required duty cycle of the converter according to the state of charge and the three stage lead acid battery charging system. The first fuzzy logic controller (FLC1) consists of an MPPT controller to extract the maximum power produced by the PV array, while the second fuzzy controller (FLC2) is aimed to control the voltage across the battery to ensure the three stage charging approach. This solution of employing two distinct cascaded fuzzy controllers surmounts the drawbacks of the classical chargers in which the voltage provided to the lead acid battery is not constant owing to the effects of the MPPT control which can automatically damage the battery. Thus, the suggested control strategy has the benefit of extracting the full power against the PV array, avoiding battery damage incurred by variable MPPT voltage and increasing the battery’s lifespan

An hybrid control strategy design for Photovoltaic battery charger

This work presents the design and the modelling of an improved lead acid Battery charger for solar photovoltaic applications. In this context, the control unit of the battery charger is composed of two intelligent controllers. In the first state, an MPPT controller based on an Adaptive neuro-fuzzy inference system (ANFIS) is used to extract the full maximum power provided by the PV array, in the second stage, the control unit switches to the regulator mode on the basis of a fuzzy logic control block that offers the three charging stages according to DIN 41773 standard for lead-acid battery. In order to demonstrate the performance of the ANFIS controller, this paper presents also a comparison of several MPPT techniques for solar PV applications

Application analysis of ANFIS strategy for greenhouse climate parameters prediction: Internal temperature and internal relative humidity case of study

The present paper, introduces Adaptive Neuro Fuzzy Inference System (ANFIS) as one of the most mature and intelligent methods to predicte internal temperature and relative humidity of a greenhouse system. To conduct the application of the proposed strategy, an experimenntal greenhouse equipied with several sensors and actuators is engaged. In this sense a data base was collected during a period of day time where the temperature and relative humidity dynamics were observed inpresence of others climatic parameters and the actuators’ actions. The results demonstrate that by using ANFIS method, the predictions match the target points with a good accuracy. Therefore, the effectiveness of the strategy in term of both inside climate parameters’ prediction is guaranteed