25_integrated virtual and remote lab for greenhouse climate control

Integrated virtual and remote lab for greenhouse climate control

J. L. Rivas*, J. L. Guzmán*, F. Rodríguez*, M. Berenguel*, S. Dormido**

*Dpto. Lenguajes y Computación. Universidad de Almería. The Agrifood Campus of International Excellence (ceiA3, Spain)

Ctra. Sacramento s/n, Almería 04120 Email: [jlrivas, joguzman, frrodrig, beren]@ual.es

**Dpto. Informática y Automática. UNED

C/. Juan del Rosal 19, Madrid 28040 Email: [email protected]

Abstract: This work presents the development of an integrated virtual and remote lab for teaching greenhouse climate control and its inclusion in a collaborative platform. The virtual lab is based on a greenhouse nonlinear model, which is used by students to understand modeling and control issues learned in theoretical lessons. The remote lab is connected to a greenhouse scale model where it is possible to perform typical climatic control tests. Both, virtual and remote labs, are integrated as one software application in a collaborative environment that is available on Internet, allowing the tool be accessed at anytime without space-temporal constraints and offering an educational platform where students can work in the same way as they would be in a traditional laboratory. 1

Keywords: control education, remote laboratory, virtual laboratory, greenhouse climate control.

1 This works This work has been partially funded by the following projects: DPI2007-61068, DPI2010-21589-C05-04 and DPI2011-27818-C02-01 (financed by the Spanish Ministry of Science and Innovation and ERDF funds).

1. INTRODUCTION

The great development of Information and Communications Technology (ICT) has allowed to renew and to evolve traditional study techniques and teaching methods by adding new educational tools, as for instance, forums, social network for education, interactive tools, virtual labs, etc. These tools allow educators to adapt to the new technological era and, at the same time, to enhance the students’ motivation.

This technological evolution has been also presented in many others fields, as the agro-alimentary field or agriculture, where new advances in automation and control engineering have been included to face the increasingly demanding conditions of the sector (Rodríguez et al., 2006).

Followings these ideas, since some years ago, greenhouse automation is being included as a subject in many agricultural engineering curricula, and some software applications have been developed to help agronomy students and researchers to understand, implement, and use the new technological advances in the sector. Agricultural engineering is one of the most important degrees of the University of Almería (Spain), and for that reason, a great effort has been performed during the last years to develop new courses and new tools focused on these ideas. A web-based remote lab for teaching greenhouse climate control techniques was created at (Guzmán et al., 2005a). Afterwards, a virtual course on

modern automation of agricultural systems was presented in (Rodríguez et al., 2004), and a virtual lab developed in (Guzman et al., 2005b). These three tools are being used successfully in a combined manner for teaching and learning issues related to greenhouse climatic control in undergraduate and doctorate courses. However, according to the students’ feedback and the teachers’ experience, they present a global drawback regarding to usability. The problem is that these three tools were developed in different platforms and the graphical user interfaces (GUI), the interactive capabilities, and the accessibility are quite different. Furthermore, the connection between the tools is quite tedious from an education point of view. Therefore, there is a need to integrate all these tools in a unique platform in such a way that the main graphical options are sharing by the virtual and remote labs, and the tools are combined facilitating information exchange.

Hence, this paper presents the development of an integrated virtual and remote lab, which has been mainly developed using Easy Java Simulation (EJS) (Esquembre, 2004). Then, the resulting lab is integrated in a collaborative environment, called eMersion (Gillet et al., 2005), and included in a national Spanish network of virtual and remote labs, AutomatL@bs (AutomatL@bs, 2009). There are some other successful examples, in the control engineering field, that have developed following this same idea and that can be visited at AutomatL@bs (2009).

Proceedings of the 9th IFAC Symposium Advances in ControlEducationThe International Federation of Automatic ControlNizhny Novgorod, Russia, June 19-21, 2012

978-3-902823-01-4/12/$20.00 © 2012 IFAC 264 10.3182/20120619-3-RU-2024.00025

The paper is organized as follows. Section 2 presents the virtual lab, describing its main features and its different software components. Afterwards, section 3 presents the remote lab, making a hardware description of the greenhouse scale model and the software architecture involved to connect the real plant with the remote lab. In section 4, the integration of the labs in eMersion and AutomatL@bs is shown. Finally, conclusions are exposed in section 5.

2. VIRTUAL LAB

As commented above, the virtual lab developed in this paper is based on the preliminary version created in (Guzman et al., 2005b). The virtual lab simulates the climate conditions of a greenhouse based on a nonlinear model implemented in Matlab/Simulink (Rodríguez, 2002). The previous version of this lab could be only executed in local mode, being necessary to have a local copy of the model for a specific version of Matlab/Simulink. With the new version, the virtual lab is prepared to be executed by several users simultaneously through Internet, having only one copy of the model in a server. To make that possible, the internal software architecture of the lab has been modified and new software components have been added. A description of the process and different components needed to develop the virtual lab are presented below.

2.1 Greenhouse climatic model

The dynamic behavior of the microclimate inside the greenhouse is a combination of physical processes involving energy transfer (radiation and heat) and mass balance (water vapor fluxes and CO2 concentration). On the other hand, the crop growth and yield mainly depend, among other conditions as irrigation and fertilizers, on the greenhouse inside temperature, the PAR radiation, and the inside humidity (see Figure 1). Thus, both climate conditions and crop growth yield influence on each other, and their dynamic behavior can be characterized by different time scales. These phenomenons were implemented using a first-principle model in (Rodríguez, 2002) in order to capture the main dynamics of the greenhouse climate.

Inside temperature

Inside PAR radiation

Inside humidity

Vents

Heating system

Shade screen

Outsidetemperature

Outsideradiation

Windspeed Wind

direction

Outsidehumidity

Disturbances

Control variables Controlled variablesLeaf area index

Skytemperature

Rain

Covertemperature

Soiltemperature

Inside temperature

Inside PAR radiation

Inside humidity

Vents

Heating system

Shade screen

Outsidetemperature

Outsideradiation

Windspeed Wind

direction

Outsidehumidity

Disturbances

Control variables Controlled variablesLeaf area index

Skytemperature

Rain

Covertemperature

Soiltemperature

Fig. 1. Climatic control variables

Therefore, this model is used in the virtual lab to test different control algorithms and to control the main variables that influence in the production process. To carry out that

control, common actuators are used, such as zenital and lateral ventilation, shade screen, and heating system. Furthermore, real data from an industrial greenhouse are used as inputs to simulate the model, being possible to modify the parameters of the control algorithms and analyze the resulting simulations.

2.2 Remote model management

As mentioned above, the virtual lab has been developed using EJS. In EJS, model equations can be included directly in the programming environment or can be connected to an existing model implemented on Simulink. Therefore, as the model developed in (Rodríguez, 2002) was implemented in Simulink, this last option was used. When the first option is selected to design the model, there is not problem to execute the virtual lab in local or remote mode due to the model is implied inside the applet. However, when the model is connected with Simulink, the problem is that users need to have a copy of the model together with the virtual lab, with the associated drawbacks (different model versions among users, needs to have Matlab/Simulink installed, etc.).

Thus, the aim is to have the Simulink model in a server, and then each client of the virtual lab can access to that server to execute an instance of the model remotely. To make that possible is necessary to use an interface that can communicate EJS’ variables to variables of the blocks in the Simulink model and vice versa on Internet. That interface is the server Java Internet Matlab (JIM), which was developed by (Farias et al. 2006). JIM allows the remote communication between a Simulink model and an application developed in EJS, being this transparent for the user. The communication is Client/Server and it is solved using TCP/IP sockets. First, the JIM server is configured with all the Simulink models that can be accessed remotely. Second, the virtual lab in EJS is configured indicating the server IP address and the name of the Simulink model. Then, input and output variables of the model can be linked to EJS variables remotely, where EJS client send input parameters to the model, and the model returns the results of its simulation through JIM. A global schema of communication between EJS and Simulink using JIM is shown in Figure 2. As seen in figure 2, now client EJS does not need to have installed Matlab/Simulink because server JIM is responsible for executing the model.

Fig. 2. Connection between EJS, JIM, and Matlab/Simulink

9th IFAC Symposium Advances in Control EducationNizhny Novgorod, Russia, June 19-21, 2012

265

2.3 GUI description

The GUI of the virtual lab is mainly divided into three parts as such as shown in Figure 3: a greenhouse synoptic, a panel of parameters to interact with the simulation, and a set of graphics, where the evolution of the most important variables in the simulation and control signals is represented.

In the upper left part of the GUI is located the greenhouse synoptic, which contains a virtual representation of a greenhouse. In this zone it is possible to monitor the crop growth and the effect of the control signals through the actuators movements: natural ventilation, shade screen, and heating based on a boiler. The lateral and zenital ventilations are represented in sky blue colour and their positions are changed taking into account the value of the associated control signal. The actual value of the vents opening is shown in percentage below the zenital ventilations and next to the right side ventilation (see Figure 3). The shade screen is located between the greenhouse and its cover; it is represented in grey colour and its length is modified based on the associated control signal. The third actuator is the heating. This system has been represented with a boiler, a pump and several pipes. A colour code is used: pump off/on - blue/green; pipes off/on – grey/based on their temperature. The value of the pipes temperature is shown next to the boiler. The crop growth is considered as a disturbance for the system being possible to modify this parameter in the graphic screen. Several plants are shown in the synoptic and their size is changed based on the value of this disturbance. Some effects have also been represented to distinguish between the day and the night. During the day the sun intensity is changed based on the solar radiation obtained from the model or the disturbances database. During the night, the sun changes to grey colour and several stars appear.

Fig. 3. GUI of the virtual lab

The set of parameters are available below the greenhouse synoptic. The parameter panel is divided in three parts. On the left side, the sliders to configure the setpoints of the control algorithms are located together with an option to choose between day or night condition of the simulation. On the right side, the different control algorithms are available, which are grouped into three tabs. The two first tabs contain parameters related to temperature control with ventilation, while the last tab has parameters to heating control and the radiation control using a shade screen (see (Guzmán et. al. 2005b) for a detailed description of these parameters). The third area is located below the control parameter tab. This zone contains the buttons to control the execution, and also to switch between virtual and remote lab (Button Connect or Disconnect). When it is labelled as Connect, this button allows the transition from virtual to remote lab, and when the same button is labelled as Disconnect is used to back from the remote version to the virtual one.

Finally, the right side of the GUI is dedicated to show six graphics representing the evolution of the main variables involved simulation, the controlled variables, the disturbances, and the control signals. The graphics are divided in three tabs. The first tab is focused on temperature control, showing the inside temperature, the outside temperature, the day and night setpoints, and the control signals for the ventilation and heating systems. In the second tab, the radiation and the humidity variables are shown. Finally, the third tab is dedicated to show the wind speed and a specific graphic to show the gain variation of the gain scheduling controller based on the outside temperature and the wind speed (Guzmán et. al. 2005b).

Notice that most of these options were available in the previous version and detailed information can be found in (Guzmán et. al. 2005b).

3. REMOTE LAB

The real plant to be accessed remotely consists in a greenhouse scale model provided with several sensors and actuators. A detailed description of this scale model can be found in (Guzmán et al., 2005a). In (Guzmán et al., 2005a), a preliminary web-based remote lab was developed using LabVIEW as the server platform. This work presented several drawbacks related to the temperature control since the actuators were discontinuous and then the use of the control algorithms was quite limited (considering the level given in typical lessons where this greenhouse scale model is used). Furthermore, this remote lab was totally different to virtual lab previously described. Therefore, the students should learn two different applications with different GUI and no exchange information was possible. Hence, it was required to develop a new remote lab to be integrated with the previous virtual lab in the same application and looking for fulfilling the requirements to be included in the AutomatL@bs project.

The following sections briefly describe the greenhouse scale model emphasizing the new additional hardware included, and the software architecture used to connect real plant and remote lab.


266

3.1 Greenhouse scale model

The greenhouse scale model, showed in Figure 4, was developed under the framework of the DAMOCIA project (see (Guzmán et al., 2005a) and the references therein). Along these years, the original greenhouse scale model has been modified, adding new sensors and actuators to improve and increase its utility. Currently, the real plant has the following sensors to measure the inside main variables: inside air temperature and humidity, and inside PAR radiation. An external meteorological station is also available to allow measuring outside temperature, humidity, wind speed and direction. Respect to additional hardware needed to simulate the real conditions of a greenhouse, a 500 Watts light is situated onto the scaled greenhouse to simulate the solar radiation, adapting its intensity to the time system. To simulate the falling temperature at night, a DC ventilator is used at a constant voltage. The same idea, but using a small heater, is used to simulate the rising temperature when windows of the greenhouse are closed during the day. From the actuators point of view, to control the daytime temperature, the same DC ventilator used previously is used adapting its voltage depending on the control signal, together with a natural ventilation system with DC motors to control the aperture. The temperature at night is also controlled, adapting the power of the small heater, accompanied with a set of red leds that are illuminated at the same time (to facilitate the remote visualization during the night). To control the radiation, a shade screen is available controlled by a DC motor. All DC motors and leds are managed using a set of relays controlled by the serial port, while an I/O card (ICP DAS Company) is used to control and to acquire data from rest of actuators and sensors.

Fig. 4. Greenhouse scale model

3.2 Remote control management

The programming environment LabVIEW has been used for the input/output tasks in order to acquired data from the sensors and modify the state of the actuators. With LabVIEW is possible to control the hardware described above from a computer using their corresponding drivers (VISA LabVIEW driver for serial port, and ICP DAS LabVIEW driver for the I/O card). The LabVIEW application created is responsible for managing the data acquisition and the execution of control loops in the scaled greenhouse. This application implements the same options available in the virtual lab commented in section 2.3, including, same options of execution, control algorithms, and data output. It has been done it this way in order to be integrated under the same virtual lab GUI developed in EJS. This integration has been possible by using JIL server (Vargas et al. 2009), which allows communicating parameters remotely between EJS and LabVIEW. The philosophy is the same applied in the virtual lab between the Simulink model, JIM server, and EJS, but now, instead of a model, the process is represented by the real plant, and where JIL server plays the role of the JIM server. Figure 5 shows a scheme of the architecture used for that purpose.

Fig. 5. Global scheme of the remote lab

Once the LabVIEW application is created, it can be published on JIL, and theirs input/output parameters are then automatically available to be referenced from EJS, abstracting developers from low-level details of programming. To access published LabVIEW variables, in EJS it is necessary to indicate the IP address and port of the JIL server, and location of the LabVIEW application inside the server. Afterwards, EJS variables can be linked to the LabVIEW ones. Following this process, the GUI described for the virtual lab in section 2.3 can be reused to control the real plant in exactly the same manner, with the same execution options, and using the same parameters for control algorithms. This is one of the most important contributions of this paper, since now the same GUI is used for virtual and remote activities.

3.3 Remote capabilities

One of the most important aspects in a remote lab is that users can obtain visual feedback of what is happening in the real plant. For this purpose, the remote lab is associated with a video server together with an IP camera, which is located inside the scaled greenhouse, allowing users to watch the actions of actuator in real time. Images captured from the camera are integrated in the user interface explained in


267

section 2.3, replacing the synoptic of the simulation for the remote lab. The GUI of the remote lab can be seen in Figure 6. Another feature offered by the remote lab is the possibility to perform experiments in order to get the model of the real plant, so that users can calculate the parameters for the controllers and algorithms to control the climate correctly. Therefore, the remote lab has available a new tab (open-loop steps) in the control panel parameters through which users can carry out open-loop steps on the heater and the ventilator. The user has also available a dynamic menu on the upper side of the application (eJournal) with two options to save the data during the experiments (Save graph, Save data/Stop Save data). Data obtained through this menu are saved in the personal user’s space inside the collaborative environment, so users can manage data after experiments. The eJournal menu is also available for the virtual lab to save data of the simulations.

Fig. 6. GUI of the remote lab

4. COLLABORATIVE ENVIRONMENT

Virtual and remote lab presented in this work has been designed to be included in the AutomatL@bs project.

AutomatL@bs is a network of virtual and remote labs for teaching control and automation, which consists of labs and educational resources provided by universities that comprise it. Users of these universities can use any lab of the network. As main feature, all labs that make up the project AutomatL@bs are developed by following certain design guidelines and technology, what allows to unify them all within an educational collaborative environment based on Web called eMersion (eMersion, 2004; Gillet, et. al. 2005). This integration allows users to work with all labs in the same way, without caring about where they really are located. Moreover, eMersion provides a virtual workspace for users (eJournal), where they can save and load their experiments

and simulations, or share their knowledge with rest of users, including teachers. This feature gives to users the opportunity to behave as if they were in a magisterial class at the real laboratory. Other important aspect, from the educational point of view, is that all labs have associated a complete guide with information, describing the GUI of the lab, tasks to do, and a complete explanation of processes that occur in the real plant with some appendix. All these documents have been carefully designed to allow students make the practical experiences in an autonomous way at their own pace engineering (AutomatL@bs, 2009).

To guarantee exclusive user access to remote labs without conflicts, AutomatL@bs has a reservation management system in which user can see the availability of a selected remote lab, and book in consequence. There are also restrictions in time about the number of hours that a user can book the remote lab continuously; avoiding one user can grab the remote lab.

Currently, the virtual and remote lab present in this paper is integrated in AutomatL@bs, in order to be available for universities belonging to the project in next academic year. Figure 7 show an image of the lab in remote mode integrated in the collaborative environment.

5. CONCLUSIONS

This paper has presented the development of a virtual and remote lab for greenhouse climate control. The resulting lab is based on preliminary versions of virtual and remote labs existing at the University of Almería. These two previous labs have been modified and improved in order to have a unique software application covering virtual and remote capabilities. In this way, the server architecture has been modified where the nonlinear model is accessed remotely and where remote accessed to the real system has been also included in the EJS application. Two bridge applications, JIM and JIL, have been used to make it possible. The integration in one application, and, with only one GUI, not only solves problems presented with previous labs, but also reports some advantages:

Now, users only have to learn once how the GUI works, since both virtual and remote labs share the same configuration parameters and the same graphics.

Users can understand better that same controllers can be applied on a model and on a real system..

Furthermore, the new lab has been development using EJS and following the required design guidelines to be integrated in the AutomatL@bs project using eMersion. Therefore, the resulting lab can take all advantages that a collaborative environment as eMersion provides, and being, at the same time, available to be used by other universities of AutomatL@bs network.

ACKNOWLEDGMENTS

The authors would like to thank Mr. Héctor Vargas and Mr. Gonzalo Farias for their help and suggestions.


268

REFERENCES

AutomatL@bs (2009). Web site of AutomatL@bs project. http://lab.dia.uned.es/automatlab.

eMersion (2004). Web site of eMersion project. http://lawww.epfl.ch.

Esquembre, F. (2004). Easy Java Simulations: A Software Tool to Create Scientific simulations in Java. Computer Physics Comm, 156(2), 199–204.

Farias, G.; Esquembre, F.; Sánchez, J.; Dormido, S. (2006). Laboratorios virtuales remotos usando easy java simulations y simulink. XXVII Jornadas de Automática. Almería.

Gillet, D., Nguyen, A., Rekik, Y. (2005). Collaborative web-based experimentation in flexible engineering education. IEEE Transactions on Education, 48(4), 696-704.

Guzmán, J.L., Berenguel, M., Rodríguez, F., Dormido, S. (2005a). Web-based Remote Control Laboratory using a Greenhouse Scale Model. Computer Applications in Engineering Education. 13(2), 111-124.

Guzmán, J.L., Berenguel, M., Rodríguez, F., Dormido, S. (2005b). Virtual lab for teaching greenhouse climatic control, 16th IFAC world congress, Prague, Czech Republic.

Rodríguez, F., Berenguel, M., Guzmán, J.L., Dormido. S. (2006). A virtual course on automation of agricultural systems. The International Journal of Engineering Education: Especial issue on agricultural/ biosystem/biological engineering education, 22(6), 1197-1210.

Rodríguez, F. (2002). Modelling and hierarchical control of greenhouse crop production. Ph.D. Thesis. University of Almería, Spain, 366 pp.

Vargas, H., Sánchez J., Salzmann, C., Esquembre, F., Dormido, S., Gillet, D. (2009). Web-Enabled Remote Scientific Environments, Computing in Science and Engineering, 11(3), 36-46.

Fig. 7. Virtual and remote lab integrated in the collaborative environment eMersion.


269

25_integrated virtual and remote lab for greenhouse climate control

Documents