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FPGA Implementation of a Robust MPPT of a

Photovoltaic System Using a Fuzzy Logic Controller

Based on Incremental and Conductance Algorithm

Mohamed Yassine Allani

UR-LAPER, Faculty of Sciences of Tunis,
University of Tunis ElManar,

Tunis, Tunisia

medyassine.allani@fst.utm.tn

Fernando Tadeo

Department of Systems and Automation Engineering,

University of Valladolid,
Valladolid, Spain

Fernando.Tadeo@uva.es

Dhafer Mezghani

UR-LAPER, Faculty of Sciences of Tunis
University of Tunis ElManar,

Tunis, Tunisia

dhafer.mezghani@fst.utm.tn

Abdelkader Mami

UR-LAPER, Faculty of Sciences of Tunis,

University of Tunis ElManar,
Tunis, Tunisia

abdelkader.mami@fst.utm.tn

Abstract—Climate dependence requires robust control of the

photovoltaic system. The current paper is divided in two main

sections: the first part is dedicated to compare and evaluate the

behaviors of three different maximum power point tracking
(MPPT) techniques applied to photovoltaic energy systems,

which are: incremental and conductance (IC), perturb and

observe (P&O) and fuzzy logic controller (FLC) based on

incremental and conductance. A model of a photovoltaic

generator and DC/DC buck converter with different MPPT

techniques is simulated and compared using Matlab/Simulink
software. The comparison results show that the fuzzy controller

is more effective in terms of response time, power loss and

disturbances around the operating point. IC and P&O methods

are effective but sensitive to high-frequency noise, less stable and

present more oscillations around the PPM. In the second section,

the FPGA platform is used to implement the proposed control.

The FLC architecture is implemented on an FPGA Spartan 3E
using the ISE Design Suite software. Simulation results showed
the effectiveness of the proposed fuzzy logic controller.

Keywords-fuzzy logic controller; P&O; IC; MPPT; energy

solar; buck converter; FPGA

I. INTRODUCTION

The electrical energy produced by photovoltaic systems is
very interesting and a modern topic of power generation.
However, the costs of PV arrays are relatively high, and the
efficiency of the energy conversion is quite low. Therefore, the
electric power generated by the PV arrays should be efficiently
used. In a direct coupled (with no battery storage) PV system,
the solar cell arrays are directly connected to the motor load
couple. These systems are relatively simple and inexpensive to
operate. A direct coupled system may include a maximum
power point tracker (MPPT) to improve its performance at

starting and at steady state operation whenever it is needed.
Many researches on photovoltaic techniques [1-3] have been
carried out in order to increase the efficiency of these panels,
and to extract their maximum energy while designing reliable
and stable systems. There are different methods of PPM
tracking such as IC [4-6], parasitic capacitance method [7],
P&O method [8], etc. They are based on the regulation of the
current or voltage of the photovoltaic generator according to
climatic conditions. As a result, the uncertainties associated
with these conditions have decreased the reliability of these
techniques, especially as a result of changes in sunlight and
temperature. Artificial intelligence techniques such as neural
networks [9] and fuzzy logic [10, 11] have the characteristics
of adaptive and versatile mechanisms and are able to improve
the performance of the control system in the presence of these
uncertainties.

In addition, a software implementation of the FLC
developed on general purpose cannot be regarded as a suitable
solution for such applications, especially when it is used as an
MPPT controller for autonomous PV applications. In this case,
FLC was usually implemented in microcontrollers [12] and/or
in digital signal processors (DSPs) [13]. Simulation and
experimentation results showed the advantages of FLC over
conventional P&O. This proposed technique was able to reduce
steady-state oscillations and improve the convergence speed of
the operating point. These devices can provide considerable
performance, but they do not offer the advantages that FPGA
(field-programmable gate array) devices can offer for the
implementation of certain commands. With the progress in
microelectronics, real time applications use intelligent
techniques and new hardware design solutions such as FPGA.
They are suitable for the implementation of control algorithms
such as fuzzy logic controllers. These circuits allowed the

Corresponding author: Mohamed Yassine Allani

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reduction of the execution time by adopting parallel processing,
rapid design prototyping and improved MPPT control of the
photovoltaic panel quality by exploiting new digital system
technologies [9, 15, 16]. From the results of these works, it can
be deduced that the MPPT is reached quickly, especially under
rapidly changing climatic conditions, the response time is
improved, the exceedance and the oscillations are extremely
reduced and therefore the energy losses are reduced. All these
advantages of real time implementation form a necessity for
MPPT control.

II. MODELLING OF THE PV CONVERSION SYSTEM

A. Modelling of the Photovoltaic Panel

The PV module converts light into electricity. The electrical
characteristics of the photovoltaic panel depend on the
irradiation and the temperature. Figure 1 shows the complete
system simulated in Matlab/Simulink. The photovoltaic cell is
modeled by (1)-(6) [16-18] that define the static behavior of the
P-N junction of a conventional diode. The electrical model of
the PV cells is shown in Figure 2.

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! (3)
�� � ��� " ��#$%&

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,-./�
� " 0�#$% �
0
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(5)

� � �� � �� �
�'�
>�?�@
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� * � 1� � '�?�
�2�2A * (6)

Fig. 1. Simulink model of the photovoltaic conversion system

Fig. 2. Simulink model of the photovoltaic conversion system

The PV parameters used in the model are summarized in

Table I.

TABLE I. PARAMETERS OF THE PV PANEL KANEKA G-SA060 [16-18]

Parameters of PV Values and units

Pmax 60.3W

Cells per Module 61

Vpv at Pmax 67V

Ipv at Pmax 0.9A

Vo (open circuit voltage) 91.8V

Is (short circuit current) 1.19A

a 2.1922

Rs (series resistance) 5.8Ω

Rsh (shunt resistance) 254.8Ω

B. Modelling of the Buck Converter

Buck converter is a DC-DC converter. It adjusts the energy
transfer between the photovoltaic generator (PVG) and the DC
load and allows the reduction of the voltage. The buck
converter model is illustrated in Figure 3. The output voltage
and current of the buck converter are expressed in:

BC � D ∗ BF (7)
�C � >1/D@ ∗ �F (8)

where α is the duty cycle of the buck converter, 0<α<1.

Fig. 3. Electrical circuit model of the buck converter.

The parameters of the buck converter connected to the PV
generator are presented in Table II.

TABLE II. BUCK CONVERTER PARAMETERS AND VALUES

Parameters of the buck converter Values and units

Vin 61V

Vout 24V

L (inductance) 10µH

Ce (input capacitor) 470µF

Co (otput capacitor) 470µF

F (switching frequency) 30kHz

III. MPPT CONTROL

Three methods are studied and developed in this work:

A. MPPT Based Perturb and Observe

Perturb and Observe (P&O) is one of the most popular
MPPT techniques used in a photovoltaic power system. This
method uses the current/voltage measurements delivered by the
PVG to calculate maximum power. Figure 4 describes the
operation of the algorithm (P & O).

B. MPPT Based on Incremental and Conductance

The IC method is often applied in photovoltaic systems. It
tracks the MPP by comparing the instantaneous and
incremental conductance of the source under consideration.
The question asked of the IC method is similar to that of P&O.

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The fixed step size is generally adopted, which determines the
accuracy and speed of the MPPT response. Figure 5 illustrates
the algorithm of the IC method.

Fig. 4. Methodology of P&O method

Fig. 5. Algorithm of the incremental and conductance method

C. MPPT Based Fuzzy Logic Control (Based on IC)

Recently, several methods have been used to extract
maximum power of the PVG [4-11, 9, 14-24]. In order to
design an MPPT controller not only efficient, but also able to
operate properly under all variations, we used fuzzy logic
control which is one of the most known control techniques of
the MPPT [10, 11, 21-23]. In addition, the IC method is often
applied in photovoltaic systems. It tracks the MPP by
comparing the instantaneous and incremental conductance of
the source under consideration. The issues of the IC method are
similar to the ones of the P&O method. The fuzzy logic
converges quickly, although it can operate with inaccurate
inputs, and does not require a perfectly well-known
mathematical model. Therefore, we have combined the IC
method into a fuzzy logic controller. The fuzzy controller
inputs are modeled by (9) and (10), the result of the
incremental and conductance algorithm is presented in (8) [4-
6].

dI
−= (9)

dI
kE +=)( (10)

)1()()( −−= kEkEkdE (11)
The proposed control model simulated with the use of

Matlab/Simulink is shown in Figure 6.

Fig. 6. Simulink model of the fuzzy logic controller

Triangular membership functions for the fuzzification
process are employed. For inputs E, dE and Duty output, 5
belonging functions have been identified depending on the
following linguistic variables: Grand negative (GN), Negative
(N), Zero (Z), Positive (P) and Grand positive (GP). The range
for the error is (0.02 to 0.2), for the error change is (-0.4 to 0.4)
and for the duty cycle increment is (-0.1 to 0.1). The degree of
membership function of the input and the output of the PV is
described in Figure 7.

(a)

(b)

(c)

Fig. 7. The membership function of duty cycle (c) and the two inputs of

the fuzzy logic controller: (a) E, (b) dE.

According to the choice carried out for the fuzzification of
the input and the defuzzification of the output, there are 25
inference rules on the fuzzy controller database. We use the

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Mamdani method with Max-Min for the fuzzy combinations
[11]. They are presented in Table III

TABLE III. INFERENCE RULES OF THE FUZZY CONTROLLER

E\dE GN N Z P GP

GN Z Z GP GP GP

N Z Z P P P

Z P Z Z Z N

P N N N Z Z

GP GN GN GN Z Z

IV. SIMULATION OF THE SOLAR CONVERSION SYSTEM

The solar conversion system is composed of four
photovoltaic panels connected in parallel, a buck converter and
a DC load. To guarantee maximum power delivered by the
photovoltaic generator, a control based on the fuzzy logic is
implemented (based on IC). This has two inputs (the voltage
and the current delivered by the GPV) and allows the
estimation of the duty cycle used to control the chopper. The
solar radiation varies between 600 and 1000W/m

2
, and the

temperature is considered constant during the simulation. The
radiation, the output power and the duty cycle are plotted in
Figure 8. It is worth noting that the duty cycle and the power
vary according to the climatic conditions. We can notice the
rapidity and the performance of the MPPT against the variation
of the radiation. The profiles of the output voltage, the current
and the power are shown in Figure 8.

Fig. 8. Variation of duty cycle, power and radiation of the PV

When the radiation changes from 600 to 800W/m
2
, our

control increases the duty cycle from 0.31 to 0.38 and the
power from 160 to 206W. When the radiation decreases from
1000 to 700W/m

2
, the power and the duty cycle also decrease.

Hence, they vary according to the radiation behavior. Figure 9
describes the output power profiles of the photovoltaic
generator and the DC-DC buck converter for a progressive
variation of the radiation starting from 600 to 800, 1000,
700W/m

2
to the value of 900W/m

2
. We note that the tracking

of the maximum power point is efficient and without

oscillations. In addition, the efficiency is optimal and the
performance of the photovoltaic generator is greater than 94%.
The simulation results proved the robustness of the proposed
MPPT control against the variation of the solar radiation. In
Figure 10, the variations of the current and the voltage are
proportional to the variation of the power.

Fig. 9. Output power profiles of the photovoltaic generator and DC-DC

buck converter.

Fig. 10. Profile of the output current, voltage and power delivered by the

PVG.

Figure 11 shows the simulation results of FLC, IC, and
P&O algorithms, where the FLC is achieved with a fast
response time and few oscillations. In terms of response time,
the three algorithms gave the same rise time to optimal power
during each sunshine variation. The oscillation around the MPP
increased power losses and thus the efficiency of the PV
system was reduced. The FLC operates with fewer oscillations
around the MPP. The power difference between the FLC and
the IC is about 3 and 6W compared to the P&O algorithm.

V. SIMULATION AND IMPLEMENTATION OF THE FUZZY
LOGIC CONTROLLER ON FPGA

Researchers can increasingly optimize problems using
suitable platforms. This work results from the need to
investigate the fundamentals of electronic power control
strategies, using fuzzy logic. In this section, we have to
implement on the FPGA a FLC based on IC. It is applied to the
photovoltaic conversion system. In some cases, the

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characteristics and the performance of the real-time
implementation unjustified and unsatisfied the calculation of
some arithmetic operations. In addition, the wrong choice of
values can sometimes exceed the overflow limits in the results
and give incorrect values. For this, the fixed point
representation was chosen. Moreover, we must define by
calculating the number of bits (m) which designate the integer
part and the rest forms of the fractional part (n) [24].

Fig. 11. Profile of the output power delivered by the PVG with FLC, IC,

P&O and without MPPT.

For more precision and to have a synthesizable code able to
be implemented in the FPGA, it is necessary to change the data
type to fixed point and to add the IEEE-proposed library. Table
IV describes some functions used in the VHDL program.

TABLE IV. FUNCTIONS AND DATA TYPES USED IN THE
IEE_PROPOSED LIBRARY

Library added to the ISE Functions and types of data

IEEE_PROPOSED

Fixed_pkg_c.vhd

Fixed_float_types_c.vhd

Sfixed (m-1 downto n)

Ufixed (m-1 downto n)

Resize(fixed point resultat,m-1,-n)

To_ufixed(std_logic_vector valeur, m-1,-n)

To_sfixed(std_logic_vector valeur, m-1,-n)

To_slv(fixed point value)

A. Architecture of the fuzzy logic controller

Mamdani’s implication is the most popular method
dedicated for the FLC [4-6]. The fuzzy rule of this method is
expressed in (12):

)]]2(),1(max[min[)(
21

xxy kk
AAc

µµµ = (12)

where k=1, 2… 25. The various processes of the fuzzy logic
controller are described in the functional diagram presented in
Figure 12.

The Mamdani method facilitates the hardware
implementation thanks to the simple min-max structure. An
overview of the internal structure of the controller is given by
the functional diagram in Figure 12. Indeed, the fuzzification of
the two input variables provides the linguistic values and the
corresponding membership functions (B1... B5) and (B11…
B15). In fact, the connection of the pairs of antecedents in the
rule structure is based on an operator logical AND. Then all the
rules are associated with a max operation using the bloc max of
the diagram. Therefore, the weighted average method is
considered as one of the defuzzification techniques, which is

suitable for hardware implementation. Considering that the
output membership functions are usually symmetrical, the
average of the fuzzy sets can be employed as weightings for the
defuzzification process. In addition, this technique contains two
arithmetic operations: multiplication processes with a constant
and a single division process.

Fig. 12. Functional diagram of the fuzzy logic control

The inputs / outputs of the analog-to-digital converter ADC
and the analog-to-digital converter DAC existing in the Spartan
3E FPGA board are represented by 12 bits. Therefore, the size
of the integer part and the decimal part of each input/output
signal is chosen according to the actual value of the signal to be
represented. For example, the input signals can be represented
by a vector of 12 bits (m=7, n=5) because the value of the
voltage cannot exceed the open circuit voltage which is equal
to 92V. On the other hand, the output value varies between 0
and 1, thus, we have represented the duty cycle by 12 bits. For
more precision, we chose to represent the decimal part by 10
bits.

B. Simulation Results

After having detailed our architecture, we have to simulate
and implement it. The simulation results using the ISE
Simulator are shown in Figure 13.

Fig. 13. Simulation results of the fuzzy logic controller

After simulation, we notice that the output of the fuzzy
logic controller d (duty cycle) tracked the variation of the input
variables Vpv (voltage) and Ipv (current). In addition, the
output PWM changes after any variation of the duty cycle d.
The input variables Vpv and Ipv are represented by 12 bits: 7
bits for the integer number and 5 bits for the fractional number.
Thereby, the output variable d is designed by 12 bits: 2 bits of
the integer number and 10 bits of the fractional one. Table V
shows the simulation results of FLC inputs and outputs whose
data are represented in real and hexadecimal numbers.

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TABLE V. SIMULATION RESULTS OF THE INPUTS/OUTPUTS OF THE
PROPOSED CONTROLLER

Ppv (W) Vpv (V) Ipv (A) d (real) d (Hexa)

8,48 10.6 0.8 0.3339 156

92.46 40.2 2.3 0.3574 16e

195 60 3.25 0.3837 189

12.6 10.5 1.2 0.3466 163

241.2 67 3.6 0.373 17e

C. RTL Architecture of the Proposed Control

The proposed architecture shown in Figure 14 is a black
box with inputs/outputs. The role of fuzzy logic is to process
inputs and interpret them to give accurate outputs. This box is
divided into different blocks that are interconnected with
intermediate signals. The RTL architecture of the fuzzy logic
controller obtained after the synthesis process by the Xilinx
Synthesis Technology (XST) is illustrated in Figure 15.

Fig. 14. The behavioral description of the controller

Fig. 15. RTL architecture of the controller

The FLC's RTL architecture is divided into 7 blocks. The
role of the first block (Interface1_u) is the calculation of the
controller inputs, the second block (fuzzify_u) plays the role of
fuzzification. Then, the (infer_u) block is dedicated to creating
fuzzy rules. The (defuzz_u) block allows reconverting the
fuzzy output to the crisp output. Furthermore, the FPGA board
card generates a frequency of 50MHz whereas the duty cycle is
generated at a frequency of 30kHz. Therefore, the (divider_cl)
block acts as a frequency divider. Indeed, the block (mux_D1)
represents a flip-flop that puts the duty cycle to the controller
output. The (mux_pwm) block is the intermediary between the
control signal and the transistor component.

D. SynthesisRreport

To verify the architecture of the proposed system,
simulations were performed using the ISE Design Suite,
version 14.7. Information about the device utilization, after the
synthesis process are given in Figure 16. The hardware
resources used by the architecture of the proposed controller
are negligible compared to the available ones on the chip.

Fig. 16. The synthesis report

E. Comparison and Discussion

Having obtained the proposed controller simulation results
in real time, it is important to verify and justify these results.
Thus, we compared the behavior of the fuzzy controller in both
environments (Matlab/Simulink and ISE) for the following
values of input variables (solar radiation between 600 and
1000W/m

2
, temperature equal to 25°C) during 5s. We

compared the results of simulation of the duty cycle presented
in Figure 8 and the values obtained in Table V. Figure 17
shows shows the coherent profile of the duty cycle carried out
in both environments.

Fig. 17. Comparison of the two simulation results.

The duty cycle varied between 0.33 and 0.4 for the same
variation of the solar radiation (from 600 to 1000W/m

2
). It is

optimally changed after the variation of the solar radiation to
track a maximal power from the PV module. The effectiveness
of this method is evaluated with different simulation studies
under Matlab/Simulink and ISE Design Suite. The proposed
architecture of the MPPT was proved to be effective.
Moreover, the output simulation by the ISE Design Suite is
approximately identical to the one carried out using
Matlab/Simulink environment. This study shows that the
FPGA circuit can be used to develop a cost-effective and
adaptable implementation of the MPPT control.

VI. CONCLUSION

The difficulties and the non-linearity of PV systems are
influenced by the solar radiation and the temperature
conditions, resulting in power losses and reduced efficiency. In
the present paper, we developed a PVG controlled by a fuzzy
logic control based on the incremental and the conductance
using Matlab/Simulink environment. The proposed MPPT
algorithm was compared with the traditional methods for
nonlinear systems (P&O and IC). The conventional P&O and
IC algorithms work with accepted time responses,
minimization of maximum power, and more oscillations, which
reduce the overall efficiency of the PV system. The proposed
FLC has reduced these disadvantages according to different
values of solar radiation. The simulation results indicated that

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the control with FLC is more efficient than the traditional
methods. It has the advantage of reducing the disturbances
when the PVG reaches its maximum power and as a result, the
performance of the photovoltaic installation has been
optimized. On the other hand, to reach a fast response time and
to take into account the time constraints, we adopted a
functional diagram to facilitate the VHDL programming and to
make this control executable in real time. Thereby, the fuzzy
logic controller based on the incremental and the conductance
was developed and simulated in VHDL. The comparison of
both results showed that the output parameters (duty cycle)
reach all variations of the input parameters (voltage and
current) of the GPV. Therefore, this study showed that the
proposed architecture was an adequate solution for a real-time
implementation. The implementation of FLC on FPGA
increases the efficiency of MPPT control systems. In future
works, we aim to apply the proposed strategy to other energy
applications.

NOMENCLATURE

PVG Photovoltaic generator

FPGA VHSIC Hardware Description Language

I Output current of the PVG

Iph Photocurrent of the solar panel

Io Photovoltaic reserve saturation current diode

a Ideality factor

k Boltzmann constant

kI Temperature coefficient in current

T Temperature

Tref Reference temperature

q Elementary charge in Coulomb

G Irradiance

Gref Reference irradiance

Egap The energy Gap

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