Embedded fuzzy controller for water level control

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 1, February 2022, pp. 277~284
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i1.pp277-284  277
Journal homepage: http://ijece.iaescore.com
Embedded fuzzy controller for water level control
Javier Eduardo Martínez Baquero1
, Jairo David Cuero Ortega1
, Robinson Jiménez-Moreno2
1
Faculty of Basic Sciences and Engineering, Universidad de los Llanos, Villavicencio, Colombia
2
Faculty of Engineering, Mechatronic Engineering, Universidad Militar Nueva Granada, Bogota, Colombia
Article Info ABSTRACT
Article history:
Received Apr 27, 2021
Revised Jul 15, 2021
Accepted Aug 2, 2021
This article presents the design of a fuzzy controller embedded in a
microcontroller aimed at implementing a low-cost, modular process control
system. The fuzzy system's construction is based on a classical proportional
and derivative controller, where inputs of error and its derivate depend on
the difference between the desired setpoint and the actual level; the goal is to
control the water level of coupled tanks. The process is oriented to control
based on the knowledge that facilitates the adjustment of the output variable
without complex mathematical modeling. In different response tests of the
fuzzy controller, a maximum over-impulse greater than 8% or a steady-state
error greater than 2.1% was not evidenced when varying the setpoint.
Keywords:
Coupled tanks
Embedded system
Fuzzy control
This is an open access article under the CC BY-SA license.
Corresponding Author:
Javier Eduardo Martinez Baquero
Faculty of Basic Sciences and Engineering, Universidad de los Llanos
Transversal 25 #13-34, Villavicencio, Colombia
Email: jmartinez@unillanos.edu.co
1. INTRODUCTION
In industrial automation processes, the control of variables is required to maintain the desired
operating conditions. For example, Abdullah and Ali present in [1] an application of motor's torque control
using a classical controller proportional, integral, derivative, or proportional integral derivative (PID)
controller. Rachedi et al. in [2] presents a speed controller design for a wind turbine is presented, and in [3]
the control of a DC-DC converter. For water management systems oriented to applications such as precision
agriculture [4], the use of control strategies is also required, which can be very varied, as explained in [5]-[7].
System modeling can be highly complex and present non-linearities, making controller design
difficult. Techniques such as fuzzy systems allow obtaining a very close approximation of their response
without an established mathematical model [8], [9]. Since the mathematical model is a necessity for the
design of control strategies, fuzzy logic presents multiple examples of its adaptation to avoid this requirement
[10]-[12]. The applications range from control of robotic arms [13], control of the speed of motors [14],
including the case previously referred to for speed control of a wind turbine [15].
The basic PID control scheme in fuzzy systems is based on Mamdani and Takagi Sugeno models
[16], [17], in such a way that by being able to control an actuator [18] such as a water filling solenoid valve
to tanks, a fuzzy controller can be applied to this case [19]. Unlike the examples presented previously, low-
cost portable systems capable of embedding a fuzzy algorithm for level control are rare; this is precisely the
scope of this work. Control algorithms can be embedded at low cost into microcontrollers [20], [21], in the
same way that fuzzy systems are found in state-of-the-art, oriented to internet of things (IoT) data acquisition
[22], and implemented in microcontrollers [23] as low-cost solutions. To address this last issue, some authors
have embedded a fuzzy controller into a microcontroller system that makes measurements of the level and
controls an actuator to regulate a storage tank's water level. Those previous works are about intelligent

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 12, No. 1, February 2022: 277-284
278
algorithms [24], [25]. The paper is divided into four sections. The first is the previous introduction; then, the
second section explains the research method, the fuzzy system, and the tank level system. The third section
exposes the results and discussion of the level control and its analysis, and finally, section four reveals the
conclusions achieved.
2. RESEARCH METHOD
The level control system consists of a workbench with two coupled water storage tanks as shown in
Figure 1. The water is entered, employing an electric pump activated using a relay commanded by the micro
controlled controller. The controller design is based on two stages, the first corresponding to the hardware
and the second to the software. These are described next.
2.1. Hardware
The MBED NXP LPC1768 microcontroller as shown in Figure 2 was chosen for programming the
fuzzy algorithm, reading the ultrasonic sensor for level, and the command of the activation relay of the filling
pump. This microcontroller has ARM Cortex-M3 core 32 Bit running at 96 MHz, includes 512 KB flash
memory, 32 KB RAM. The Mbed is an easy-to-use tool, has a C/C++ programming environment, with an
ARM-based compilation engine, suitable for fuzzy algorithm integration has a small size (54x26 mm) and a
low cost (USD 80).
The level measurement is generated with an SRF06 ultrasound sensor with a current output with a
range of 4-20 mA, see Figure 3. This sensor is powered by a current loop and does not require other power;
its measurement range is 2 cm to 510 cm. The current output is four mA for a zero cm measurement and
20 mA at 510 cm; the above gives a nominal current of 4 mA +31.37 uA/cm. The SRF06 requires a loop
drive voltage of 9 V to 24 V, operates continuously automatically, and varies every 70-100 ms.
A single-phase KSI240 industrial relay with Triac or SCR at its output is used to activate the electric
pump. It is usually used in industrial applications. This relay can be used for resistive, inductive, and
capacitive loads. The control voltage is 4 to 32 Vdc or 85 to 280 Vac, with a current output of 10 A, 25 A,
40 A, 60 A, 80 A at 48-280 Vac, see Figure 4.
Figure 1. Coupled tanks station Figure 2. MBED NXP LPC1768 microcontroller
Figure 3. SRF 06 ultrasonic sensor Figure 4. KSI240D25 relay

Int J Elec & Comp Eng ISSN: 2088-8708 
Embedded fuzzy controller for water level control (Javier Eduardo Martínez Baquero)
279
The level controller design implies elaborating of a circuit for the synchronization signal from the
microcontroller to the single-phase network or called a zero-crossing detector circuit. It is also necessary to
generate a control signal for the electric pump motor, which corresponds to a signal pulse-width modulation
(PWM) to turn the solid state relay (SSR) on or off. Figure 5 shows the circuit and the zero-crossing
simulation signals. The yellow signal corresponds to the rectification stage's signal. The green one
corresponds to the voltage on the 4N25 optocoupler LED, the purple signal to the voltage on the
optocoupler's resistance, which means the crossover detector's output. Likewise, the blue signal corresponds
to conditioning to the input signal through a comparison and saturation stage. Its stage employs an LM324N
and a 74HC14N that create the necessary pulse to enter the microcontroller.
Motor control requires the operation of the solid-state relay. The relay has a zero-crossing
activation, regardless of the moment of the half-cycle in which the input signal is excited, waiting for the next
zero-crossing to activate or deactivate the relay. Therefore, the minimum variation to be made for the PWM
will be a half cycle as shown in Figure 6. This technique is known as zero cross turn-on and differs from the
phase control technique called random turn on [5]. The Mbed microcontroller has a PWM output assigned to
the output device and its respective variation in the whole operation of the SSR.
Figure 5. Circuit and the zero-crossing simulation signals
Figure 6. Expected and obtained output signal

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2.2. Software
A fuzzy Mamdani-type controller with proportional, integral, and derivative (PID) actions is
embedded inside the microcontroller, with zero integral action and initially simulated in MATLAB as shown in
Figure 7. The controller's two inputs correspond to the error and the derivative of the error and the output to
the percentage of the required PWM signal's duty cycle. The pulse width-level relationship has been
previously parameterized and identified; this allows identifying the PWM signal's duty-cycle and establishing
a setpoint through the rule base. For example, a 67% duty cycle or controller output with a 20 ms period
PWM signal allows setting a setpoint at 10 cm.
Figure 8 allows to appreciate the input membership functions used in the controller design. The
universe of discourse covers the range from 0 to 250 centimeters for the input error, and a maximum
variation percentage of 10 percent was defined for the rate of change of the derivative input. Linguistic labels
of medium-low (ml), low (l), zero (z), high (h) and medium-high (mh) were used [26]. Figure 9 shows the
output membership functions for the duty cycle of the PWM signal, with linguistic labels of super-low (SL),
low low (LL), low (L), medium (M), high (H), and high (HH) and super high (SH).
Figure 7. Mamdani fuzzy block and the activation of each of the rules
Figure 8. Defining input sets
Simulation of the controller allows obtaining the control surface illustrated in Figure 10, where the
gradual increase in the output of the duty cycle is seen, smoothed, and continuous, which determines a stable
control action for the system. Once the operation of the controller is validated, it is transcribed into C
language for the microcontroller. An interface was created in LabView for the visualization of the plant level,
taking advantage of the peripherals of the serial communication microcontroller, where the level data is sent
and allows the user to visualize, through a graphic representation of the tank in real-time, the measurement
value and the drive of the electric pump as illustrated in Figure 11.

281
Figure 9. Effects of selecting different switching under dynamic condition
Figure 10. Rule base generated surface, output, and inputs
Figure 11. Visualization software
3. RESULTS AND DISCUSSION
Once the designs and simulations are done, the controller circuits are implemented. Figure 12 shows
the power circuit to power the microcontroller, an LCD as a local display interface, a keyboard, and the level
sensor. Figure 13 shows the controller circuit. It contains the Mbed microcontroller that, using a matrix keyboard
and the LCD, performs the reading and entry of parameters for the level control system's proper operation.

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Figure 12. Sensor supply circuit and zero crossing
Figure 13. Control circuit. Mbed, 4x4 keyboard and 16x2 LCD
The SRF06 sensor used has a protective casing as shown in Figure 14, making it robust to the
environment, preventing it from being exposed to water and dust that can occur in a harsh work environment.
For the development of the prototype, a restored workbench carcass was used and later modified to use water
storage tanks. As a result, a stable, robust, and broad structure for level control is obtained. Table 1 shows the
performance of the controller when adjusting different set-points. The steady-state error is evidenced, which
is low, and in turn the response time obtained to reach the system establishment is evidenced. In no case was
there evidence of maximum overshoot exceedance of 8%.
Figure 14. SRF06 ultrasonic sensor installation (4-20 mA)

283
Table 1. The performance of fuzzy controller
Setpoint Ess Tr
180 cm 1.8% 223 seg
200 cm 1.92% 268 seg
220 cm 2.03% 311 seg
4. CONCLUSION
It was possible to obtain a prototype with independent power and remote supervision through
LabView, which efficiently controls the desired water level of coupled tanks. The embedded controller
results in a portable system that can be adapted to other control schemes with only an additional tuning
process. The computational model of the fuzzy controller enables establishing the design conditions based on
the two types of input sets, the error, and its derivative. Also, it allows generating the rule base for the control
action and variation of specific input setpoints through the knowledge-based analysis of the system. Lastly,
the stability and the system response analysis showed a suitable performance under the expert criteria, based
on the simplicity of the final design and the desired control parameters.
ACKNOWLEDGEMENTS
The authors are grateful to Universidad de los Llanos and Universidad Militar Nueva Granada for
the funding of this project.
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BIOGRAPHIES OF AUTHORS
Javier Eduardo Martinez Baquero is an Electronic Engineer graduated from
Universidad de los Llanos in 2002. Postgraduate in Electronic Instrumentation from
Universidad Santo Tomas in 2004, Postgraduate in Industrial Instrumentation and Control
from Universidad de los Llanos in 2020 and M.Sc. Educative Technology in Universidad
Autonoma de Bucaramanga in 2013. He can be contacted at email:jmartinez@unillanos.edu.co.
Jairo David Cuero Ortega is an Electronic Engineer graduated from Universidad
de los Llanos (Unillanos) in 2012. He also holds a postgraduate degree in Industrial
Instrumentation and Control from the same university. Currently, he is studying Master of
Engineering-Industrial automation at Universidad Nacional de Colombia. He works as a
professor at Unillanos, and his research scopes are Instrumentation, Control and Robotics. He
can be contacted at email: jairo_cuero@unillanos.edu.co.
Robinson Jiménez-Moreno is an Electronic Engineer graduated from
Universidad Francisco José de Caldas in 2002. He received a M.Sc. in Engineering from
Universidad Nacional de Colombia in 2012 and Ph.D. in Engineering at Universidad Francisco
José de Caldas in 2018. Currently, he is working as an assistant professor of Universidad
Militar Nueva Granada and his research focuses on convolutional neural networks for object
recognition and image processing for robotic applications such as human-machine interaction.
He can be contacted at email: robinson.jimenez@unimilitar.edu.co.

Embedded fuzzy controller for water level control

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