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An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse


In the recent past home automation has been expanding its objectives towards new solutions both inside the smart home and in its outdoor spaces, where several new technologies are available. This work has developed an approach to integrate intelligent microclimatic greenhouse control into integrated home automation. Microclimatic control of greenhouses is a critical issue in agricultural practices, due to often common sudden daily variation of climatic conditions, and to its potentially detrimental effect on plant growth. A greenhouse is a complex thermodynamic system where indoor temperature and relative humidity have to be closely monitored to facilitate plant growth and production.

This work shows an adaptive control system tailored to regulate microclimatic conditions in a greenhouse, by using an innovative combination of soft computing applications. In particular, a neural network solution has been proposed in order to forecast the climatic behavior of greenhouse, while a parallel fuzzy scheme approach is carried out in order to adjust the air speed of fan-coil and its temperature. The proposed combined approach provides a better control of greenhouse climatic conditions due to the system’s capability to base instantaneous solutions both on real measured variables and on forecasted climatic change. Several simulation campaigns were carried out to perform accurate neural network and fuzzy schemes, aimed at obtaining respectively a minimum forecasted error value and a more appropriate fuzzification and de-fuzzification process.

A Matlab/Simulink solution implemented with a combined approach and its relevant obtained performance is also shown in present study, demonstrating that through controlled parameters it will be possible to maintain a better level of indoor climatic conditions. In the present work we prove how with a forecast of outside temperature at the next time-instant and rule-based controller monitoring of cooling or heating air temperatures and air velocities of devices that regulate the indoor micro-climate inside, a better adjustment of the conditions of comfort for crops is achievable.


The microclimate control in a greenhouse is a complex task since it is affected by several interdependent variables; this makes the application of the conventional techniques for indoor climate regulation (cooling, ventilation, heating) often difficult. While the indoor thermal conditions of residential building depend on objective factors (type of construction) and subjective factors (individual comfort), the optimal climate conditions for the growth of a crop in a greenhouse may be considered objective because they depend exclusively on the type of crop. However, the greenhouse internal climate conditions are subject to quick variations due to unpredictable and unstable external climate parameters.


In order to test the proposed approach, a greenhouse scenario need to be formalized. Several conventional designs are reported in the literature. They are usually have five steps. The first step deals with understanding the physical system and its control requirements; subsequently it is possible to develop a model, which includes the plant, sensors and actuators. The third step is on the use of a linear control representation in order to determine a detailed version of the controller. The fourth step aims at simplifying the complex previous representation. The control system is divided in several sub-systems. The calculation proceeds with the development of different algorithms, one for each sub-system controlled.


Figure 1. PFCS System Model. ANN: artificial neural network; and FLC: fuzzy logic controller

Figure 1. PFCS System Model. ANN: artificial neural network; and FLC: fuzzy logic controller

The operating principle of the proposed system is shown in Figure 1. It is based on a parallel control fuzzy scheme (PCFS). The advantage of this model is that the fuzzy logic is characterized by linguistic rules, rather than complex mathematical formulations. Climate parameters are recorded in time steps preceding the time of forecast (t + 1); they are used in order to infer the trend of indoor temperature within the greenhouse. The outputs of the ANN are therefore the forecasted indoor temperatures, calculated by model at each time step.

Figure 2. Parallel control fuzzy scheme (PCFS)

Figure 2. Parallel control fuzzy scheme (PCFS)

This approach is applicable to all weather-climatic parameters that influence the behavior of the micro-climate inside the greenhouse. Figure 2 shows an example on which the tests described are based and the relative results which validate the proposed method. The approach described proves that just by forecasting outside temperature at the time t + 1 (created by ANN) and by controlling internal temperature through FLC, a better control of the optimal conditions can be achieved. The approach could be easily extended to the prediction of variation and control of other typical parameters of a greenhouse, i.e., humidity.


Figure 5. Error comparison during learning test

Figure 5. Error comparison during learning test

Figure 5 shows how the proposed ANN achieves comparable results, in terms of prediction error during learning phase, by using a very simple model that could be implemented in embedded devices used in controlled greenhouses.

Figure 7. PCFS performance

Figure 7. PCFS performance

Figure 7 shows air speed and temperature as a function of the time during an evaluated summer day. At 1:00 p.m., the shown control values (inlet air speed and its temperature) do not appear to depend on the action taken at 12:00 a.m. In fact, the values appear as if the system turns on for the first time at a given hour (at 12:00 a.m.).


In this paper, an application of a combined neuro-fuzzy model for dynamic and automatic control of greenhouse climatic parameters has been proposed. The approach shows a couple of FLCs fed by a climatic temperature predictor. The temperature forecast is carried out by an NNARX model, which attained good forecasting performances. In fact, results show a low error related to data approximation. Moreover, the results of proposed approach allow us to strike a good compromise between the fan coil speed and its temperature in order to increase the crop productivity.

This solution could be used for different crops as it is feasible to adapt it in different production greenhouses and allows for shaping the control mechanism of PCFS in several scenarios. The results show that the efficient and dynamical regulation of the on/off times of the air speed of fan-coil and its temperature allows us to achieve a more efficient use of energy. Some experiments in a real test-bed over several days could be realized in a follow up of this work. Moreover, a further application of an integration in a smart home system of an improved controller, based on other characteristics which affect the crops of a garden, such as humidity, cloudiness, and different types of solar radiation, is a goal of our future research works.

Source: Kore University of Enna
Authors: Giuseppina Nicolosi | Roberto Volpe | Antonio Messineo

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