Fuzzy Control of a Water Pump for an Agricultural Plant Growth System
Jos´e Dias
1
, Jo˜ao Paulo Coelho
1,2
and Jos´e Gonc¸alves
1,2
1
Instituto Polit´ecnico de Braganc¸a, Escola Superior de Tecnologia e Gest˜ao, Campus de Sta. Apol´onia,
5300-253 Braganc¸a, Portugal
2
INESC TEC Technology and Science, Campus da FEUP, 4200 - 465, Porto, Portugal
Keywords:
Renewable Energy, Fuzzy Controller, Agriculture.
Abstract:
At the present time there is a high pressure toward the improvement of all the production processes. Those
improvements can be sensed in several directions in particular those that involve energy efficiency. The defi-
nition of tight energy efficiency improvement policies is transversal to several operational areas ranging from
industry to public services. As can be expected, agricultural processes are not immune to this tendency. This
statement takes more severe contours when dealing with indoor productions where it is required to artificially
control the climate inside the building or a partial growing zone. Regarding the latter, this paper presents an
innovative system that improves energy efficiency of a trees growing platform. This new system requires the
control of both a water pump and a gas heating system based on information provided by an array of sen-
sors. In order to do this, a multi-input, multi-output regulator was implemented by means of a Fuzzy logic
control strategy. Presented results show that it is possible to simultaneously keep track of the desired growing
temperature set-point while maintaining actuators stress within an acceptable range.
1 INTRODUCTION
Nowadays, in order to increase the companies com-
petitiveness, all the production processes are subject
to rigorous audits. The output of such studies lead to
changes in several key variables that will, ultimately,
lead to a performance increase. Those changes can
happen at several levels, ranging from plant layout,
tasks scheduling, rework or scrap minimization and,
of course, energy efficiency.
Energy efficiency is a pressing problem that is ad-
dressed at severallevels. Inparticular,at the European
Union, there are currently several political mecha-
nisms undergoing in order to both reduce the energy
consumption and also to promote renewable-based
energy production systems (Armstrong and Blundell,
2007).
However economic growth is tightly connected to
energy consumption. Hence, in order to cope with all
those constraints one must be able to find alternative
strategies to devise more energy efficient production
methods that will not scale up with the required eco-
nomic development.
As can be expected, agricultural processes are not
immune to this tendency. Within a global market
paradigm, the costs reduction while maintaining the
products quality, is a constant grower concern. Hence
maintaining market competitiveness can then be a
challenge tackled at both operational and technologi-
cal fronts.
Regarding the latter, often it is possible to trans-
pose solutions used in different contexts to alternative
application bringing value to a new process. For ex-
ample the water heating systems, based on thermody-
namic pumps used in domestic applications, can be
also used in agricultural processes as is the case of
greenhouses productions.
This paper deals with a new devised strategy to
improve the energy efficiency of a tree nursing sta-
tion. This new solution replaces the actual resistor
based heating system by an alternative using circula-
tion of hot water in a closed pipe circuit. The water
recirculation is performed by an electric pump and the
heating is taken from the environment by means of a
set of thermodynamic panels.
However, prior to the effective implementation of
this solution, it is required to test if this alternative
heating system can, in fact, be used to replace the old
one. In order to do this a scaled version of a growing
platform was built where the thermodynamic heating
system was replaced by a butane gas water heater.
Both water pump and gas heating system must be
156
Dias, J., Coelho, J. and Gonçalves, J..
Fuzzy Control of a Water Pump for an Agricultural Plant Growth System.
In Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015) - Volume 2: FCTA, pages 156-161
ISBN: 978-989-758-157-1
Copyright
c
2015 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
controlled taking into consideration the imposed tem-
perature set-point while maintaining system integrity.
The actuators state will be function of exogenous in-
formation provided by an array of sensors and the
control law. In this work a Fuzzy controller strategy
was pursued due mainly to the fact that, under this
paradigm, a expert type controller is easily translated
to a Fuzzy behaviour system (Kia et al., 2009).
The remain of this paper will be divided into four
additional sections. After a more thorough presenta-
tion of the addressed problem at section 2 the Fuzzy
controller design strategy will be described along sec-
tion 3. The obtained results, regarding both set-point
tracking and actuator wearing, will be presented at
section 4. Finally some concluding remarks, as long
as future work trends, will be presented in section 5.
2 PROBLEM DESCRIPTION
In this section the addressed problem will be dis-
cussed in further deep. In order to do that some previ-
ous contextualization about the actual installed trees
growing system will be provided. This system con-
sists on platforms with around six meters of length
and near two meters width placed inside a greenhouse
where the air temperature is roughly controlled. In
particular, the inside air temperature ranges, in aver-
age, from 10 to 30 degrees centigrade.
At the present time the installed growing machine
is used to nurse olive trees and chestnuts, among oth-
ers, until they are sufficiently resistant to be planted
outdoors (Jacobs et al., 2009). The system is a tray,
covered with perlite, and with an electric resistance
coil format, powering 3 kW, running along the cov-
ered area. Actually the greenhouse has 9 growing
stands leading to an installed power of near 30 kW.
A picture of the above referred growing platform sys-
tem is depicted in Figure 1.
During the winter time, when the indoor temper-
atures are lower, the average energy spent in each
growing platforms is, in average, around 60 kWh per
day. This number is excessive and import high pro-
duction costs. In order to reduce this value an alterna-
tive heating system was devised by replacing the in-
efficient resistance based heating element by an water
heating system drive by thermodynamic panels (Yang
et al., 2007) (Huiling and Xiangzhao, 2012).
The heating process is straightforward as can be
exemplified in the diagram of Figure 2.
Before implementing this heating system, and
since the heat and flow requirements of the growing
stand, under nominal and extreme conditions, is un-
known, a scale prototype was built. First the stand
Figure 1: The currently installed growing platform inside
the greenhouse. The center pipes are for irrigation purposes.
Figure 2: Block diagram of the growing stand heating sys-
tem.
was implemented using the same structure material
as the original. Only its dimensions was scaled to a
platform of 125 cm × 65 cm. The height of 18 cm
was deliberately let equal to the original in order for
the solution devised for the prototype to be easily in-
tegrated thereafter in the real system.
The piping system was embedded in a special
modelled styrofoam as shown in Figure 3. Then this
assembly was placed inside the metal frame and filled
with perlite.
In the test rig developed, the thermodynamic heat-
ing system was replaced by a butane gas heating. This
operating change was mainly due to controllability
and logistics. Nevertheless, the information one seeks
to find could be also easily obtained by this new ap-
proach. Figure 4 shows an image of this alternative
structure.
Moreover an array of sensors was dispersed along
the system in several key points. Six waterproof tem-
perature sensors (DS18B20) were distributed along
the perlite surface. This set of sensors allows to anal-
yse how well and uniform the heat spreads along the
working area. In addition other temperature sensors
are available: one to measure the heater outlet water
temperature, the water temperature at a reservoir and
the environment air temperature. The water flow is
also measured by means of a flow sensor.
The water is made circulating by means of a 1/2
HP water pump system as can be observed from Fig-
Fuzzy Control of a Water Pump for an Agricultural Plant Growth System
157
Figure 3: The growing stand internal piping system mean-
der around a piece of styrofoam used in radiant oors do-
mestic heating systems.
Figure 4: Distribution of the temperature sensors along the
perlite.
ure 5. The water pump is connected to the gas heater
system by means of 1 inch pipes. On the other hand,
the heater outlet pipe feeds the growing table. The
circuit is closed by means of a return pipe from the
table to the reservoir.
The control and data acquisition system was built
around an Arduino Uno R3.0 (Dhamakale and Patil,
2011). Most temperature sensors connects to the mi-
crocontroller board by means of One-Wire communi-
cation protocol. Others require the use of an analog
to digital (A/D) input. The time is kept tracked us-
ing a real-time clock (RTC) and data is locally saved
in a secure digital (SD) memory card. The former
is connected to the Arduino platform by means of
inter-integrated circuit communication protocol (I
2
C)
and the latter by serial peripheral interface (SPI). This
hardware setup can be observed from Figure 6.
The trees nursing process requires that the surface
Figure 5: Detail on the water circulating system. Below the
table it is possible to observe both the water pump and the
reservoir.
Figure 6: Control and data acquisition hardware built
around an Arduino Uno board.
temperature be maintained around 23 degrees centi-
grade. In this process the main external disturbances
are the indoor air temperature fluctuations and the
load imposed by periodic irrigations. However, in this
work, the latter is not considered.
In order to cope with the requiredsurface tempera-
ture set-point two manipulated variables are available.
The heater and the water pump states. Both have an
important role in temperature regulation that will be
further explained in the next section along with the
control law devised to regulate this system.
3 THE FUZZY CONTROLLER
Besides set-point tracking, the addressed system also
requires water temperature supervision in order to
FCTA 2015 - 7th International Conference on Fuzzy Computation Theory and Applications
158
prevent the system collapse. Moreover, due to the ac-
tuators nature, a bang-bang approach to control must
be performed. The use of an approximated first or-
der system with low bandwidth, a classical approach
to controller system design should not be a challenge.
However, and in order to approximate de controller
behaviour by an expert controller point-of-view, a
Fuzzy based controller was devised (Dhamakale and
Patil, 2011).
The controller design was carried out using the
MATLAB
R
Fuzzy Logic Toolbox taking into consider-
ation empirical knowledge. The controller structure,
within the Fuzzy Toolbox context, can be observed
from Figure 7.
This is a multi-input, multi-output controller sys-
tem whose inputs are the average perlite surface tem-
perature and the water temperature inside the reser-
voir. The output variables are the heater and water
pump states.
The fuzzy inference mechanism selected was a
Mamdani type (Mamdani and Assilian, 1975; Sakti,
2014). Moreover the conjunction and disjunction
rules operation were the minimum and maximum re-
spectively. The defuzzification process is carried out
by means of a centroid operation over the feature
space.
Figure 7: Overall Fuzzy controller structure and
parametrization.
Regarding the surface temperature variable, the
input space was partitioned into five Gaussian type
Fuzzy sets. Each one was labelled as ‘cold’, ‘cool’,
‘good’, ‘warm’ and ‘hot’. This fragmentation can be
depicted from Figure 8.
Regarding the reservoir water temperature, the in-
put space was divided into three sets labelled ‘cold’,
“good and ‘hot’. The sets distribution along the input
space range can be observed from Figure 9.
Now the output space for the water pump was split
into three triangle type membership functions as can
be concluded from Figure 10.
The same operation was carried out for the heater
Figure 8: Surface temperature fuzzification. This input vari-
able was differentiated into five partially overlapped sets.
Figure 9: Reservoir water temperature fuzzification. Three
sets was used to describe the water temperature status over
the range from 10 to 65 degrees centigrade.
Figure 10: Output space partition for the water pump using
three triangle type membership function.
variable. However, in this case, a five membership
functions set was used to describe the output space.
Nevertheless the same triangular shape membership
functions were used as in the previous case as can be
seen from Figure 11.
The Fuzzy controller rules were produced auto-
matically by the MATLAB
R
software. In particular
a total of 15 rules were produced. This rule based
controller structure can be easily analysed by means
Fuzzy Control of a Water Pump for an Agricultural Plant Growth System
159
Figure 11: Output space partition for the gas heating system
using five triangular shape membership functions.
of a graphical output available from the toolbox. A
screenshot of this rule viewer graphical tool is pre-
sented in Figure 12.
Figure 12: The rule viewer assuming an surface temperature
of 23 degrees centigrade and a reservoir water temperature
of 37.5 degrees centigrade.
In the next section the above designed Fuzzy con-
troller behaviour will be presented. Its performance
will be analysed regardingboth set-point tracking and
actuators stress. Even if the former figure of merit can
be slightly relaxed the latter is of extreme importance
in order to reduce wearing of both the water pump and
the gas heating system by preventing high frequency
switching states.
4 RESULTS AND DISCUSSION
Several tests were performed, always giving priority
to the worst case scenario, with ambient temperatures
close to 10 degrees centigrade. In all the performed
tests the desired set-point temperature is considered
constant and equal to 23 degrees centigrade. Below
are presented the results of two tests. The first, whose
results are illustrated in Figure 13, has a temperature
starting point of around about 16 degrees centigrade.
Figure 13: Results obtained during the first test. From top
to bottom one can observe the following curves: Deposit
temperature, set-point, average temperature, ambient tem-
perature and heater status.
A second experiment, with lower water tempera-
ture starting point, was also performed. In this second
case the initial system temperature is approximately
14 degrees centigradeand the averagetemperature en-
vironment stays around 11 degrees centigrade. The
obtained results are presented on Figure 14.
Figure 14: Results obtained during the second test. From
top to bottom one can observe the following curves: De-
posit temperature, set-point, average temperature, ambient
temperature and heater status.
From both experiments it is possible to conclude
that, in both cases, the controller was able to reach,
and maintain, the set-point temperature. Moreover
one can see that the closed loop system exhibits an
over-damped response type. In addition it is possible
to observe the heater state change during the opera-
tion. Notice that the water pump state did not change
during the experiments and was always on. From the
obtained data it is possible to conclude that the rate of
change of the heater state has a period of around 30
minutes. Hence the designed controllerdid not lead to
an aggressive closed loop response from the actuators
point-of-view.
FCTA 2015 - 7th International Conference on Fuzzy Computation Theory and Applications
160
5 CONCLUSION
This paper addresses the implementation of a Fuzzy
controller for a new tree nursing station heating sys-
tem based on thermodynamic panels. This alternative
strategy, if proven economically viable, will replace
the older system whose heating system is based on
electric heating elements. In order to infer the appli-
cability of this technique a scaled test rig was built.
This test rig was used to test if it was possible to at-
tain the set-point temperature in the worst operating
conditions and, if so, what will be the water tempera-
ture required and its mass flow. In the test rig the wa-
ter was heated, not using the thermodynamic heating
system, but a gas boiler whose state could be easily
controlled and minimizing logistic problems. In addi-
tion, a half horse power water pump was installed, in
order to make the water circulating. The pump opera-
tion was controlled by a simple on-off strategy.
In order to maintain the system state variables in
the required range a multi-input, multi-output Fuzzy
controller was designed. The controller input are the
temperature information collected by an array of sen-
sors and its output signal are the commands for both
the water pump and the gas heater. The choice of this
controller paradigm was due mainly to its simplicity
and its proximity to empirical control based on expert
knowledge.
From the obtained results it is possible to con-
clude that the designed controller was able to main-
tain the average surface temperature at the set-point
level without generating any high frequency control
signals. This is very important in order to reduce
the wearing inherent to rapid on-off actuator state
changes.
However further tests must be still performed us-
ing different operating conditions like increased air
temperature, adding moisture to the perlite substrate
since, in real operating conditions, the irrigation sys-
tem periodically sprinkle the small trees. It is neces-
sary to analyse how this change in thermal conductiv-
ity will be noticed in the overall system performance.
ACKNOWLEDGEMENTS
This work is financed by the ERDF - European Re-
gional Development Fund through the COMPETE
Programme (operational programme for competitive-
ness) and by National Funds through the FCT -
Fundac¸˜ao para a Ciˆencia e a Tecnologia (Portuguese
Foundation for Science and Technology) within
project “*FCOMP-01-0124-FEDER-037281*”.
REFERENCES
Armstrong, F. and Blundell, K. (2007). Energy - beyond oil.
Oxford University Press.
Dhamakale, D. and Patil, S. (2011). Fuzzy logic approach
with microcontroller for climate controlling in green
house. International Journal on Emerging Technolo-
gies, 2:17–19.
Huiling, Z. and Xiangzhao, F. (2012). An experimental re-
search on the application of ground source heat pump
and floor-heating system in the greenhouse. Power
and Energy Engineering Conference, pages 1–4.
Jacobs, D. F., Landis, T. D., and Luna, T. (2009). Nursery
manual for native plants. R. Dumroese et al.
Kia, P. J., Far, A. T., Omid, M., Alimardani, R., and Nader-
loo, L. (2009). Intelligent control based fuzzy logic for
automation of greenhouse irrigation system and eval-
uation in relation to conventional systems. World Ap-
plied Sciences Journal, 6(1):16–23.
Mamdani, E. and Assilian, S. (1975). An experiment in lin-
guistic synthesis with a fuzzy controller. nternational
Journal of Man-Machine studies, 7:1–13.
Sakti, I. (2014). Methodology of fuzzy logic with mamdani
fuzzy models applied to the microcontroller. Informa-
tion Technology, Computer and Electrical Engineer-
ing.
Yang, Z., Pedersen, G., Larsen, L., and Thybo, H. (2007).
Modeling and control of indoor climate using a heat
pump based floor heating system. IECON 2007 - 33rd
Annual Conference of the IEEE Industrial Electronics
Society, page 29852990.
Fuzzy Control of a Water Pump for an Agricultural Plant Growth System
161