design and implementation of takagi-sugeno fuzzy tracking

POWER ENGINEERING AND ELECTRICAL ENGINEERING VOLUME: 17 | NUMBER: 3 | 2019 | SEPTEMBER

Design and Implementation of Takagi-Sugeno FuzzyTracking Control for a DC-DC Buck Converter

Dhaouadi GUIZA1, Youcef SOUFI 2, Djamel OUNNAS 2, Abderrezak METATLA1

1Department of Mechanical Engineering, Faculty of Technology,University of Skikda, 26 Route El Hadaiek. 21000 Skikda, Algeria.

2LABGET Laboratory, Department of Electrical Engineering, Faculty of Science and Technology,University of Tebessa, Route de Constantine, 12002 Tebessa, Algeria

[email protected], [email protected], [email protected], [email protected]

DOI: 10.15598/aeee.v17i3.3126

Abstract. This paper presents the design and imple-mentation of a Takagi-Sugeno (T-S) fuzzy controllerfor a DC-DC buck converter using Arduino board. Theproposed fuzzy controller is able to pilot the states ofthe buck converter to track a reference model. The T-Sfuzzy model is employed, firstly, to represent exactly thedynamics of the nonlinear buck converter system, andthen the considered controller is designed on the basisof a concept called Virtual Desired Variables (VDVs).In this case, a two-stage design procedure is developed:i) determine the reference model according to the de-sired output voltage, ii) determine the fuzzy controllergains by solving a set of Linear Matrix Inequali-ties (LMIs). A digital implementation of the pro-posed T-S fuzzy controller is carried out using theATmega328P-based Microcontroller of the ArduinoUno board. Simulations and experimental resultsdemonstrate the validity and effectiveness of the pro-posed control scheme.

Keywords

Arduino board, DC-DC buck converter,T-S fuzzy model.

1. Introduction

DC-DC buck converters are widely used in industrialand home environment (mobile phone, computers, andhome appliances). Thanks to their increasingly high ef-ficiency, together with a reduced size, weight and cost,they have held an important place in connections tostorage batteries, photovoltaic systems, wind turbines,hybrid systems [1], [2], [3] and [4]. However, the con-

trol of a buck converter is still a challenging task be-cause such a system exhibits a nonlinear behavior withinherent uncertainties and disturbances. Thus, the lin-ear control schemes cannot ensure satisfactory perfor-mances over a wide operating range [5]. To address thisproblem, nonlinear and advanced control design meth-ods have been proposed, such as linearization control[6] and [7], sliding mode [8] and [9], adaptive control[10] and [11], backstepping control approach [12] andexact linearization methods [13].

The simplicity of design and the robustness of thesliding mode controller have made it the most used one[14] and [15]. But, these advantages are neutralizedby the presence of an undesirable phenomenon knownas ’chattering’ (oscillations having finite frequency andamplitude) which is considered as the main obstacle forimplementation [16] and [17]. In [18] and [19] an adap-tive backstepping controller is proposed for DC-DCbuck converters. But it also suffers from difficulties andlimitations during the implementation stage. In [20]and [21], a state feedback exact linearization method isapplied to DC-DC buck converters. However, in [20],the effects of the different parasitic elements are nottaken into account, while in [21] the developed con-troller cannot be used in a wide range of variation be-cause of the excessive output voltage overshoot.

In the field of fuzzy logic, much research has devotedto the application of fuzzy Mamdani controllers to con-verters [22] and [23]. These works are typically basedon a small signal model using the state space averag-ing method; the model obtained by these methods isonly useful for small variations around a specific oper-ating point, whereas the application of Takagi-Sugeno(T-S) fuzzy models on buck converters is not suffi-ciently investigated. The T-S fuzzy models owe theirpopularity to their effectiveness in modeling and con-

c© 2019 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 234


trolling nonlinear systems [24] and [25]. The T-S fuzzymodel represents a nonlinear system by a set of fuzzy If-Then rules, which locally represents the input-outputrelationships of a system expressing each conclusionwith a linear subsystem. The advantage of this typeof fuzzy model lies in the stability of the fuzzy systemwhich can be analyzed using the Lyapunov method,and then treated in terms of the feasibility of a set ofLinear Matrix Inequalities (LMIs). In this case, theproblem can be solved easily by numerical convex op-timization techniques [26].

Recently, the T-S fuzzy tracking control problem hasbeen addressed in many works based on a conceptknwon as Virtual Desired Variables (VDVs) to sim-plify the development of the control law and referencemodel [27], [28], [29], [30], [31] and [32]. Furthermore,the VDVs concept allows converting easily the track-ing problem into a stabilization one. The concept hasbeen analyzed in a number of studies using the Lya-punov approach and successfully investigated in manycontrol applications. For example, in [28], it was em-ployed to control a wind energy conversion system. In[29], it was employed to control a Permanent MagnetSynchronous Machine (PMSM) while in [30], the sameconcept was used to control a photovoltaic system. In[31], it was used to develop a fuzzy torque observerfor PMSM and in [32], it was combined with H infin-ity performance to design a T-S fuzzy controller fora PMSM.

In this paper, the purpose is to develop a T-S fuzzytracking controller for a buck converter based on theVDVs concept. In this case, the proposed controllercan be used to drive the system to follow the desiredreference. First, the nonlinear buck converter systemis represented by a T-S fuzzy model. Then, a fuzzycontroller is developed based on a set of virtual desiredvariables to simplify the design of the reference modeland control law. Next, the tracking performance of theenhanced fuzzy system is analyzed by the Lyapunovmethod which can be formulated into LMIs problems.Simulation tests are performed on a buck converter toverify the controller’s efficiency. Finally, the proposedcontroller is implemented on an ATmega328P-basedmicrocontroller of Arduino Uno.

The remainder of this paper is composed as follows:the Sec. 2. is devoted to details on the mathematicalbuck converter model. In the Sec. 3. the pro-posed fuzzy control method is introduced. It consistsof three main blocks: The first part deals with the T-Sfuzzy controller, the second part is dedicated to stabil-ity analysis conditions and the third one deals with thedetermination of the desired reference model and thenonlinear tracking controller. The results of the sim-ulation and practical implementation of the proposedcontroller are given in Sec. 4. and Sec. 5. followedby a conclusion at the end of this work.

2. Mathematical BuckConverter Model

The buck converter can be represented by the followingnonlinear state space system form:

x(t) = f(x(t)) + g(x(t))u(t) + η

y(t) = ϕ(x(t)), (1)

x(t) =

[iL(t)vo(t)

], η =

[−VD

L0

], (2)

f(x(t)) =

=

− 1L

(RL + RRC

R+Rc

)iL(t)− R

L(R+RC)vo(t)(R

C(R+RC)

)iL(t)−

(1

C(R+RC)

)vo(t)

, (3)

g(x(t)) =

[(1LVin + VD −RM iL(t)

)u(t)

0

], (4)

ϕ(x(t)) =

(RRC

R+Rc

)iL(t) +

(R

R+Rc

)vo(t), (5)

where RM is the resistance of the transistor (MOS-FET), RL is the winding resistance of the inductor,VD is the threshold voltage of the diode, RC is theequivalent series resistance of the filter capacitor andVin is the input voltage. iL, vo and u represent, re-spectively, the inductance current, the output voltageand the duty cycle of the buck converter, as shown inFig. 1. It should be mentioned that the internal resis-

Vin

vD

RL

L

RC R

Rm

M D

L

vo +

-

+

-

+

-

vo

iL

C -

vc

Fig. 1: Equivalent circuit of DC-DC buck converter.

tance Rm, the diode’s forward voltage VD, the equiv-alent series resistance of the filter capacitor RC andthe winding resistance of inductor RL have not beentaken into account in many previous researches, whichcan perturb the control of the buck converter system.Consequently, this paper considers a more general case.

3. Fuzzy Control Design

The goal of this paper is to develop a fuzzy con-troller that permits to pilot the states of a buck con-verter x =

[iL vo

]T to track a desired trajectoryxd =

[iLd vod

]T . Firstly, we design a fuzzy con-troller based on the T-S fuzzy model of a buck con-verter system and then develop a virtual reference



model and nonlinear controller according to the de-sired output voltage. Thus, the fuzzy tracking controlscheme shown in Fig. 2 is proposed.

Nonlinear

Tracking

Controller

TS-Fuzzy

Controller

Reference

Model

+ -

Vin

vD

RL

L

RC R

Rm

M D

L

vo +

-

+

-

+

-

vo

iL

C -

vc

vc

iL

vref

x

τ

u

T-S fuzzy control

0 0

Fig. 2: Proposed fuzzy control scheme.

3.1. T-S Fuzzy Model of BuckConverter

The development of the proposed T-S fuzzy controllergoes through the transformation of nonlinear modelEq. (1) into a fuzzy model by using the variable ofthe inductance current iL as decision variable. Thenonlinear state space of buck converter is given by thefollowing form:

x(t) = Ax(t) +B(iL)u(t) + E

y(t) = vo = Cx(t)), (6)

where:

A =

[− 1

L

(RL + RRC

R+Rc

)− R

L(R+RC)R

C(R+RC) − 1C(R+RC)

], (7)

B(iL) =

[1L (Vin + VD −RM iL(t))

0

], (8)

E =

[−VD

L0

], C =

[RRc

R+RC

RR+RC

]. (9)

Assuming that the premiss variable z(t) = iL(t) isbounded as: iL ≤ iL(t) ≤ iL and using sector nonlin-earity transformation [33], the nonlinear system Eq. (6)can be exactly represented by a T-S fuzzy model usingthe following two If-Then rules:

Rule1: If iL is F11 Then x(t) = A1x(t) +B1u(t) +E1,Rule2: If iL is F12 Then x(t) = A2x(t) +B2u(t) +E2,

where F11 and F12 are the membership functions givenby:

F11(iL) =iL(t)− iLiL − iL

, F12(iL) = 1− F11(iL). (10)

The sub-matrices are defined as:

A1 = A2 =

[− 1

L

(RL + RRC

R+Rc

)− R

L(R+RC)R


],

(11)

B1 =

[1L

(Vin + VD −RM iL

)0

], (12)

B2 =

[1L (Vin + VD −RM iL)

0

],

E1 = E2 =

[−VD

L0

].

(13)

The final output of fuzzy model is inferred as follows:

x(t) =

r∑i=1

hi(z(t)) (Aix(t) +Biu(t) + E) , (14)

where hi(z) = ωi(z)/∑r

i=1 ωi(z), ωi(z) =∏n

j=1 Fij(zj)

for all t > 0, hi(z) ≥ 0 and∑r

i=1 hi(z) = 1.

3.2. Control Design and StabilityAnalysis

The T-S fuzzy control is necessary to satisfy the fol-lowing condition:

x(t)− xd(t)→ 0 as t→∞, (15)

where xd(t) represents the desired trajectory variable.Let us define the tracking error as x(t) = x(t)− xd(t).Then, its time derivative can be given by:

˙x(t) = x(t)− xd(t). (16)

By substituting Eq. (14) into Eq. (16) and adding theterm

∑ri=1 hi(z)Ai(xd(t)− xd(t)), Eq. (16) becomes:

˙x(t) =

r∑i=1

hi(z) (Aix+Biu+Aixd(t) + Ei)− xd.

(17)In Eq. (17), let us choose a new control variable τ(t)that satisfies the following condition:

r∑i=1

hiBiτ =

r∑i=1

hi(z) (Aixd +Biu+ E)− xd. (18)

By using Eq. (18), the tracking error derivative Eq. (17)can be rewritten as follows:

˙x(t) =

r∑i=1

hi(iL(t))(Aix(t) +Biτ(t)). (19)



The new controllers are developed to deal with thefuzzy tracking control problem as:

Controller rule 1 : If iL(t) is F11 Then τ(t) = −K1x(t),Controller rule 2 : If iL(t) is F12 Then τ(t) = −K2x(t).

The final fuzzy controller output is given by:

τ(t) = −r∑

i=1

hi(z(t))Kix(t). (20)

By substituting Eq. (20) into Eq. (19), the final closed-loop system takes the following form:

˙x(t) =

r∑i=1

r∑j=1

hi(z(t))hj(z(t))(Ai −BiKj)x(t). (21)

By lettingGij = (Ai−BiKj), Eq. (21) can be rewrittenas follows:

˙x(t) =

r∑i=1

r∑j=1

hi(z(t))hj(z(t))Gij x(t). (22)

Stability Analysis. Obtaining the fuzzy controllerconsists in determining the gains Ki satisfying the con-ditions of the following theorem [28] and [29]:

Theorem 1 The continuous model Eq. (22) isasymptotically stable via the fuzzy controller Eq. (20),if there exists a diagonal matrix D, matrices Qij with:Qii = QT

ii and Qji = QTij for i 6= j, and a common

positive definite matrix P > 0 such that:

GTiiP + PGii +Qii +DPD < 0, i = 1, ..., r, (23)

(Gij +Gji

2

)T

P + P

(Gij +Gji

2

)+Qij ≤ 0,

i < j ≤ r, (24)Q11 Q12 . . . Q1r

Q12 Q22 . . . Q2r

.... . .

...Q1r Q2r . . . Qrr

≡ Q > 0, (25)

for i, j = 1, . . . , r, s.t. the pairs (i, j) such that:hi(z)hj(z) = 0, ∀ t.

The determination of the fuzzy control gains requireschanging the conditions of the previous theorem intoan equivalent problem of linear matrix inequalities.This transformation corresponds to simple objectivechanges of variables X = P−1, Ki = MiX

−1 and theuse of a congruence in inequalities Eq. (23), Eq. (24)and Eq. (25). Then, the following LMIs can be ob-tained.

∃ X = XT > 0, ∃ Yii = Y Tii , ∃ Yij = Y T

ji , ∃ Mi:[XAT

i +AiX −BiMi −MTi B

Ti + Yii XDT

DX −X

]< 0,

(26)

XATi +AiX +XAT

j +AjX −BiMj −MTj B

Ti

−BjMi −MTi B

Tj + 2Yij ≤ 0,

i < i ≤ r, (27)Y11 Y12 . . . Y1rY12 Y22 . . . Y2r...

. . ....

Y1r Y2r . . . Yrr

≡ Y > 0. (28)

3.3. Desired Reference Model andNonlinear Controller

In order to determine the desired reference model xdand nonlinear controller u(t), we use Eq. (18) whichcan be rewritten as follows:

r∑i=1

hiBi(u− τ) = −r∑

i=1

hiAixd −r∑

i=1

hiE + xd. (29)

Noting that:

A =

r∑i=1

hiAi, B =

r∑i=1

hiBi, E =

r∑i=1

hiEi. (30)

Then, Eq. (29) can be rewritten in the following form:

B(u(t)− τ(t)) = −Axd(t)− E + xd(t). (31)

Eq. (31) can be rewritten as follows:[1L [Vin + VD +RM iL(t)]

0

](u(t)− τ(t)) =

= −

[1L [RL + RRC

R+Rc] − R

L(R+RC)R


] [iLd

vcd

]+

−[−VD

L0

]+

[iLd

vcd

]. (32)

It should be mentioned that the nonlinear control lawand the desired reference model will be calculated ac-cording to the desired output voltage.

From the second equation of Eq. 32, we obtain

− R

C(R+RC)x1d +

1

C(R+RC)x2d = 0

⇒ x1d =x2dR. (33)

From the first equation of Eq. 32, we can derive thenonlinear control law u(t), as follows:

u(t) =(RL

R + RC

R+RC+ R

R+RC)x2d + VD

Vin + VD +RM iL(t). (34)



0 0.005 0.01 0.015 0.020

0.5

1

1.5

2

2.5

3

3.5

Times (s)

Cur

rent

(A

)

5 6 7 8 9 10

x 10−3

0.15

0.16

0.17

0.18

iL

iLd

(a) Inductance current.

0 0.005 0.01 0.015 0.020

2

4

6

8

Vol

tage

(V

)

vo

vod

Times (s)

(b) Output voltage.

0 0.5 1 1.5 2 2.5 3 3.5

x 10−3

0

0.2

0.4

0.6

0.8

1

Times (s)

PW

M S

igna

l

(c) PWM signal.

0 0.005 0.01 0.015 0.020

0.2

0.4

0.6

0.8

1

Times (s)

Dut

y cy

cle

u(t)

5 6 7 8 9 10

x 10−3

0.68

0.69

0.7

(d) Duty ratio.

Fig. 3: Simulation results for voltage reference vod = 8 V.

Time (s)0 0.02 0.04 0.06 0.08 0.1

Out

put V

olta

ge (

V)

0

1

2

3

4

5

6

7Desired VoltageTS ControllerPI Controller

0 0.01 0.02 0.03 0.040

1

2

3

4

5

Fig. 4: Comparison between PI and T-S fuzzy controllers.

4. Simulation Results

In order to verify the performance of the proposedfuzzy tracking control, simulation test was carried outon a DC-DC buck converter. The controller gains areobtained by solving the LMIs Eq. (26), Eq. (27) andEq. (28), as follows:

K1 =[

0.4829 0.1582], (35)

K2 =[

0.4537 0.1345]. (36)

The first simulation is carried out in output volt-age reference vod = 8 V. The responses of the induc-

Matlab/Simulink model Hardware

Communication

via USB port

Fig. 5: Communication with an Arduino board using Matlabinput/output package.

Tab. 1: Performances comparison between PI and T-S con-trollers.

Method PI controller T-S controllerRise time (s) 0.0187 6.6211 · 10−4

Settling time (s) 0.0327 0.0012Overshoot (%) 0.0264 0

tance current, output voltage, PWM signal and dutycycle are depicted in Fig. 3(a), Fig. 3(b), Fig. 3(c) andFig. 3(d), respectively. The results indicate that thesteady states track the desired trajectories perfectly.It is also shown that the time response required to fol-low the reference model is very short (0.0012 s). Itcan be concluded that the proposed T-S fuzzy controlhas a good tracking performance. In the second simula-tion test, the T-S fuzzy controller is compared with thebase-line PI (Proportional Integrator) controller due toits popularity. The PI parameters (Kp and Ki) are cal-culated based on the following buck transfer function



Inductor

Current sensor

Voltagesensor

Load

Arduino Uno

MOSFETDiode

Capacitor

(a) Hardware setup.

Vref

Group 1

Voltage Reference

10/1023

Conversion

to Vref

Real-Time PacerSpeedup = 1

Real-Time Pacer

Arduino1Analog Read

Pin 2

Potentiometer

SetupArduino1COM12

Arduino IO Setup

Saturation

Arduino1Analog Write

Pin 11

Arduino Analog Write

Arduino1Analog Read

Pin 0

Current Sensor

Vref xd

VDVs

xd

x

iL

Tho

T-S Fuzzy Controller

xd

iL

Tho

u

Nonlinear Controller

Manual Switch

254

Convert

to PWM

Arduino1Analog Read

Pin 1

Voltage Sensor

(b) Simulink model using Matlab input/output package.

Fig. 6: Hardware implementation using Arduino Uno and Simulink model.

[36] and [37]:

G(s) =Vou

= Vin

(R

R+RL

)s

ωZERO+ 1

Ω(s)

, (37)

where

Ω(s) =s2

ω20

+s

Qω0+ 1, (38)

ω0 =1√

LCR+Rc

R+RL

, ωZERO =1

CRc, (39)

Q =1

ω0

(L

R+RL+

RRLC

R+RL+RcC

) . (40)

The PI parameters (Kp and Ki) are obtained by us-ing the known compensation method, as follows:

Kp = 0.195, Ki = 9.88. (41)

The response of the output voltage for vod = 5 V risetime, the settling time and the overshoot for the twomethods.

From Fig. 4 and Tab. 1, it can be confirmed thatthe T-S controller offers superior performance and fastdynamic response in terms of rapidity and limitationof overshoot.

5. Experimental Verification

To verify the simulation results, a special packageknown as input/output (I/O) support package is usedwith Matlab environment, which has been designed byMathWorks for microcontroller-based Arduino board

10/1023

Conversion

to Vref

SaturationVref xd

VDVs

xd

x

iL

Tho

T-S Fuzzy Controller

xd

iL

Tho

u

Nonlinear Controller

254

Convert

to PWMPin 2

ARDUINO

Potentiometer

Pin 5

ARDUINO

PWM

Pin 0

ARDUINO

Current Sensor

Pin 1

ARDUINO

Voltage Sensor

Fig. 7: Simulink model using Matlab support package.

to interface the Matlab/ Simulink with the hardwaresetup without any programming language [34]. Thispackage is primarily used for real-time communica-tion between Arduino board and Simulink, as shownin Fig. 5. The hardware consists of a buck converter,an Arduino Uno, a voltage sensor and a current sensor.The hardware and Simulink model used in this imple-mentation stage are shown in Fig. 6(a) and Fig. 6(b),respectively. Note that the parameters given in Ap-pendix A are considered for the development of a buckconverter prototype.

The experiment is conducted with a multi-step volt-age reference. The experimental waveform and sim-ulation response of output voltage are illustrated inFig. 8(a) and Fig. 8(b). It is obvious that the obtainedresults in both the simulation and the implementationare in good agreement.

After the previous verification and validation of thesimulation results, the proposed system should be cre-ated as a standalone project that does not need to be



0 5 10 15 20 25 30 35 400

1

2

3

4

5

6

7

8

Time (s)

Vol

tage

(V

)

vod

vo

(a) Simulation results of output voltage. (b) Experimental waveform of output voltage.

Fig. 8: Simulation and experimental results for multi-step voltage reference.

connected to the Simulink (host computer) [35]. In thisrespect, it is necessary to deploy the proposed trackingcontrol algorithm to the Arduino board. To achievethis, an Arduino support package is used, as shown inFig. 7. The experimental waveforms of the output volt-age and the duty cycle for a reference voltage vod = 5 Vare shown in Fig. 9.

These practical results demonstrate that buck con-verter can be controlled by the proposed method toflow exactly the desired output voltage.

Fig. 9: Experimental results for desired voltage vod = 5 V.

6. Conclusion

A T-S fuzzy tracking controller is proposed for a DC-DC buck converter which is able to pilot the systemto follow a desired reference model. The controllergains are obtained based on sufficient conditions for-mulated into LMIs form and solved using optimizationtools. Experimental and simulation results show thatthe buck converter can be controlled effectively at dif-ferent operating regions by the proposed method andcan overcome the limitations of classical controllers.

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About Authors

Dhaouadi GUIZA was born in Tebessa, Algeria.He received a B.Sc. degree in Elecrtonics from


http://dx.doi.org/10.1002/cta.468

http://dx.doi.org/10.1016/j.rser.2016.09.076

http://dx.doi.org/10.1109/JESTPE.2017.2708815

http://dx.doi.org/10.1109/TFUZZ.2006.876725


http://dx.doi.org/10.1007/s13369-017-2532-0

http://dx.doi.org/10.1049/iet-cta.2009.0469

http://dx.doi.org/10.15598/aeee.v13i3.1331

http://dx.doi.org/10.3233/ICA-2003-10404

http://dx.doi.org/10.3182/20130828-3-UK-2039.00057

http://dx.doi.org/10.1016/j.jneumeth.2012.09.012

http://dx.doi.org/10.3390/electronics7120412



Skikda University, Algeria in 2001, Magister degree inelectronic components and system from ConstantineUniversity, Algeria in 2009. He is currently a researchfellow and Ph.D. candidate at Skikda Universitiy(Algeria). His research interests include fuzzy controlof electrical drives, energy conversion and powercontrol, practical electronics and Drives.

Youcef SOUFI was born in Tebessa, Algeria.He received a B.Sc. degree and Doctorate degreesfrom the University of Annaba, Algeria, in 1991 and2012 respectively. and a Magister degree in 1997in Electrical Engineering from Tebessa University,Algeria. Currently, he is a Professor in the departmentof Electrical Engineering, Faculty of Sciences andTechnology, Larbi Tebessi University, Tebessa, Alge-ria. His research interests include electrical machines,diagnostics, wind and solar energy, power electronicsand drives applied to renewable and sustainable energy.

Djamel OUNNAS was born in Tebessa, Alge-ria. He received a B.Sc. degree in electronic fromTebessa University, Algeria in 2007, Magister degreein automatic from Biskra University in 2011. Hegraduated with Ph.D. degree in Automatic from SetifUniversity, Algeria in 2017. Currently he is a Professorin the department of Electrical Engineering, Facultyof Sciences and Technology, Larbi Tebessi University,Tebessa, Algeria. His research interests includefault detection and diagnosis in industrial systems,

fuzzy control of electrical drives, energy conversionand power control.

Abderrezak METATLA received the Engi-neer, Magister and Ph.D. degrees in electromechanicalengineering from Badji Mokhtar University in Annaba,Algeria in 2004, 2009. Since 2004, he has been in theElectromechanical Department of Skikda University,Algeria where he is currently a Professor of electricalengineering. His main research fields include electricalmachines, control systems and renewable energy.

Appendix ABuck Converter Parameters

Circuit parameters ValueMOSFET resistance Rm 0.1 Ω

Threshold voltage of the diode VD 0.8 VOutput capacitor C 270 µF

Output capacitor resistance Rc 0.18 ΩInductor L 600 µH

Winding resistance of inductor RL 0.1 ΩResistance load R 30 ΩInput voltage Vin 10 V

Switching frequency fs 31.38 kHz


design and implementation of takagi-sugeno fuzzy tracking

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