Automation of microclimate in greenhouses

. An agricultural greenhouse is a complex system with many input features. Taking these features into consideration creates favorable conditions for the production of plants. The parameters are temperature and internal humidity, which have a significant impact on the yield. The aim of this study was to propose a dynamic simulation model in the MATLAB/Simulink environment for experimental validation. In addition, a fuzzy controller for the indoor climate of the greenhouse with an asynchronous motor for ventilation, heating, humidification, etc. has been designed. The model includes an intelligent control system for these drives in order to ensure optimal indoor climate. The dynamic model was validated by comparing simulation results with experimental measurement data. These results showed the effectiveness of the control strategy in regulating the greenhouse indoor climate.


Introduction
The greenhouse is a solution for protecting vegetation from diseases and bad weather, being a complex system. Its main function is maintaining the internal climate, which is influenced by many factors such as wind speed, solar radiation, external temperature and humidity. Two main problems have limited the expansion of greenhouse agricultural production.
First, the method of controlling and adjusting the air temperature inside the greenhouse to the needed value is fuzzy logic control. In this work, the Arvanitis 1999 model was taken as a basis for a dynamic model of an experimentally validated agricultural greenhouse, in order to create a suitable microclimate with appropriate drives installed in the greenhouse. Temperature is the main climatic variable affecting yield, therefore it is traditionally controlled in greenhouses. Based on the energy balance of the elementary volume of greenhouse air, the ratio can be calculated using the equation: Where ‫ܩܶ‬ is temperature in the greenhouse, ‫ݍܥ‬ is heat capacity of the greenhouse, ‫ܭ‬out,air is greenhouse heat loss rate, ܶ‫‬ut is outside temperature and ‫ܪܳ‬ is heating power. Thus, the difference in temperature and humidity is an important feature when simulating a greenhouse installation.
Second, the use of multiple controlled drives such as ventilation, heating and humidification/dehumidification systems makes the greenhouse an energy-intensive consumer. Therefore, efficient energy systems should be used in order to reduce operating costs.
A greenhouse is a system that can promote plant growth as it guarantees suitable microclimatic conditions for fixed cultivation. In fact, a greenhouse is a heat storage system that converts incident solar radiation into heat dissipation. This physical process is based on conduction, heat accumulation and convection. The internal microclimate control can be automated (e.g. using a fuzzy controller) only if a physical model of the greenhouse is available. This model should be able to anticipate changes in the internal environment parameters, which are based on several boundary conditions. This work provides a model of a greenhouse using fuzzy logic. The proposed greenhouse model is based on four basic layers being involved in thermodynamic exchanges: the technical structure itself, the air inside the greenhouse, plants and soil. The role of each layer is as follows: the greenhouse retains heat (the cover is usually made of plastic film or glass); the indoor air is indoor climate that is controlled by temperature and humidity; plants play a strategic role in water and heat balance due to the evaporation process; soil is characterized by absorption and diffusion of thermal radiation.
The proposed system was tested in the MATLAB/Simulink environment using a meteorological database of real measurements. This included measurements of solar radiation, wind speed, temperature, and relative humidity. It also contained thermal properties of the greenhouse response to external solar radiation.
In theoretical simulating, temperature and humidity were compared with experimental measurements. The values are correlated and subject to the normal law, distributed both by hours and over a year. The simulation of temperature and humidity in the greenhouse was carried out using differential equations, the experimental external values were measured using the RTU5023 sensor. (Fig.1). The solar radiation data are presented from the ones of the average statistics for the city of Rostov-on-Don (47.8132, 41.6217). The average annual value is close to 4 kW*h/m2 (Fig. 2).

Fuzzy logic controller for the greenhouse
The work of the investigated system is as follows: comparison of temperature (T in ) and humidity (H in ) with the specified characteristics. The difference gives ΔT and ΔH errors for the regulation of internal factors of the greenhouse, controlled by the actuators for ventilation, heating, etc. When the actuators are active, the heat flow supplied by the heating system and the air flow from the ventilation system will be part of the thermodynamic model. Therefore, the temperature and humidity inside the greenhouse are controlled using appropriate equations.
Let us build a fuzzy system in MATLAB, Figure 3. The fuzzy controller is based on a fuzzy inference engine, which consists of three main processing subsystems. The first interface converts input linguistic variables to numeric values at Fig. 4. The database unit includes fuzzy rules. Transformation processor, clear control rules at output for specific actuators. The input state variable of the fuzzy temperature controller is ΔT, where ΔT = T-T in ϵ (NB, NM, Z, PM, PB) (Fig. 4). The membership functions of the input temperature error are shown in, where NB is negative large, NM is negative mean, Z is zero, PM is positive mean, and PB is positive large.

Conclusion
After simulating the settings and sensor readings, it can be concluded that the system can work stably until a critical value appears, as a result of which the installation adjusts to system changes and stabilizes the microclimate values after automatic changes in the system.