This paper presents comparison of the performance of Fuzzy Logic Controller with that of conventional closed loop controller for buck-boost converter fed dc motor based on voltage control method. For ac to dc conversion, diode bridge rectifier-chopper configuration gives higher input power factor than that of thyristor controlled rectifiers. So, one such configuration called ac-dc buck-boost converter feeding a dc drive is taken up for a detailed study in this paper. The performance of the proposed method is investigated using MATLAB simulation models of buck-boost converter fed dc motor

Assistant Professor, John Stephen

John Stephen

S Ben

T Ruban Devaprakash

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Advances in Natural and Applied Sciences, 8(20) Special 2014, Pages: 79-84 
 
AENSI Journals 
Advances in Natural and Applied Sciences 
 
ISSN:1995-0772    EISSN: 1998-1090 
 
Journal home page: www.aensiweb.com/ANAS 
 
   
Corresponding Author: S. Ben John Stephen, Assistant Professor, Electrical and Electronics Engineering, Noorul Islam 
University, Tamil Nadu, India., 
                                      E-mail: benjohnstephen@gmail.com., Mobile : +919952262775.  
Improved Control Strategy on Buck-Boost Converter Fed DC Motor with Fuzzy Logic 
Controller 
 
1
S. Ben John Stephen and 
2
T. Ruban Devaprakash
 
 
1Assistant Professor, Electrical and Electronics Engineering, Noorul Islam University, Tamil Nadu, India. 
2Professor, Electrical and Electronics Engineering, Noorul Islam University, Tamil Nadu, India. 
 
A R T I C L E  I N F O   A B S T R A C T  
Article history: 
Received   3 September 2014 
Received in revised form 30 October 
2014 
Accepted 4 November 2014 
 
Keywords: 
buck-boost; ac-dc converter; PAM; 
dc motor; power  electronics 
controller; fuzzy logic controller 
 
 This paper presents comparison of the performance of Fuzzy Logic Controller with that 
of conventional closed loop controller for buck-boost converter fed dc motor based on 
voltage control method. For ac to dc conversion, diode bridge rectifier-chopper 
configuration gives higher input power factor than that of thyristor controlled rectifiers. 
So, one such configuration called ac-dc buck-boost converter feeding a dc drive is taken 
up for a detailed study in this paper. The performance of the proposed method is 
investigated using MATLAB simulation models of buck-boost converter fed dc motor. 
  
 
 
© 2014 AENSI Publisher All rights reserved. 
To Cite This Article: S. Ben John Stephen and T. Ruban Devaprakash., Improved Control Strategy on Buck-Boost Converter Fed DC 
Motor with Fuzzy Logic Controller. Adv. in Nat. Appl. Sci.,  8(20): 79-84, 2014 
 
INTRODUCTION 
 
Generally, the conversion of ac power to dc power is carried out using a diode bridge and a large dc 
capacitor connected to the load terminal. Such an conversion has many disadvantages, including high input 
current harmonic components and an invariable output dc voltage. Many configurations have been presented to 
minimize the harmonic current components and improve the input power factor (Prasad, A.R., P.D. Ziogas, S. 
Manias., 1990)  
Many attempts have been made using Pulse Width Modulation (PWM) techniques (Mechi, A., S. Funabiki., 
1993) to improve the performance of the switching device. The commonly used Proportional-Integral-
Derivative (PID) controllers are simple to be realized, but they suffer from poor performance if there are 
uncertainties and nonlinearities. The Fuzzy Logic Controllers have emerged as a tool for difficult control 
problems of unknown nonlinear systems.  
This paper presents Modeling and MATLAB simulation of ac–dc buck-boost converter fed dc motor with 
Pulse Area Modulation technique. It also describes the FLC design for ac-dc buck-boost converter fed dc motor 
which is simulated in MATLAB software and the simulation results are presented. Finally, the last section 
presents conclusions derived from this work. 
 
Circuit Description and Principles of Operation: 
Fig. 1 represents a separately excited dc motor fed from an ac-dc buck-boost converter. This converter 
comprises of a diode bridge, a switching device (IGBT), reactor in the dc link, a series connection of a diode and 
a capacitor parallel to the dc reactor. The step-up and step-down characteristics of the output voltage can be 
easily obtained by appropriately switching IGBT. 
 
80                                                S. Ben John Stephen and T. Ruban Devaprakash, 2014 
Advances in Natural and Applied Sciences, 8(20) Special 2014, Pages: 79-84 
 
 
 
Fig. 1: AC-DC buck-boost converter fed dc motor 
 
Pulse Area Modulation Method: 
Principle of Pulse Area Modulation: 
Fig. 2 shows the implementation PAM control circuit. The reactor voltage is amplified and fed into the 
integrating circuit. This integrating circuit is reset at a constant interval, and its output is the saw-tooth wave, 
which has a gradient that is proportional to the value of inductor current. This saw-tooth wave is compared to 
the reference wave, thereby obtaining the PWM wave that drives the switching device IGBT.  
 
 
Fig. 2: PAM Control Circuit 
 
 
 
Fig. 3: Gating Signals using PAM technique 
 
Generation of PAM Gating Signals: 
Fig. 3 illustrates the generation PAM gating signals which is employed for the control of buck-boost 
converter. The gradient of saw-tooth wave varies proportionally to reactor current. When reactor current 
gradually increases, the current becomes a saw-tooth wave whose gradient increases gradually as shown in Fig. 
3. If the reference wave has a constant voltage, the duty ratio gradually decreases. So, the input current 
81                                                S. Ben John Stephen and T. Ruban Devaprakash, 2014 
Advances in Natural and Applied Sciences, 8(20) Special 2014, Pages: 79-84 
 
waveform becomes square wave in which peak value gradually increases and the pulse width gradually 
decreases. 
The pulse shown with hatched lines has twice the peak value as that shown with dotted area, but it has one-
half of pulse width with equal area. If the reference waveform is constant, the areas of these pulses will not 
change, but if the reference waveform is changed into a sine wave as shown in Fig. 2, the input current will 
change into sine wave. Thus this controller also improves the input power factor. 
         
Simulation And Results: 
For the simulation, a dc motor of rating 180V, 8.5A, 1.5kW, 735rpm having armature resistance Rm=2.80Ω, 
armature inductance Lm=5.0mH, motor constant Km=2.11V/(rad/s) and moment of inertia J=0.25 kgm
2
 is used. 
A capacitor C of 330µF, inductor L of 95.8mH and switching frequency of 1.8 kHz have been chosen for this 
simulation.  
Fig. 4 shows the simulated motor speed characteristics using Pulse Area Modulation technique.  
 
 
Fig. 4: Simulated motor speed characteristics using PAM technique 
 
Fuzzy Logic Control Method: 
Fuzzy Logic Controller: 
The basic idea behind Fuzzy Logic Controller is to incorporate the “expert experience” of a human operator 
in the design of the controller in controlling a process whose input-output relationship is described by collection 
of fuzzy control rules involving linguistic variables rather than a complicated dynamic model. In Fuzzy Logic 
System, the linguistic variables are used instead of crisp variables. 
The typical architecture of FLC comprises of four stages namely fuzzification, fuzzy rule base, inference 
engine and defuzzification. 
 
Fuzzification: 
Fuzzification is the process of transforming crisp variables into suitable linguistic values. Instead of using 
mathematical formulas, a FLC uses fuzzy rules to make a decision and generate the control effort. The rules are 
in the form of IF-THEN statements. Actually, the solutions are based on the experience of a designer or the 
previous knowledge of the system. In this paper, the knowledge from the PAM controller borrowed first to help 
to define rules. 
In this paper, motor speed is the variable. The inputs to the FLC are error e(k) and change in error ∆e(k). 
The output is the duty cycle d(k). The equation for error and change in error are given in Eq. 1 and Eq. 2. 
E = e k = wr k −w k .                                                                                                                                     (1) 
 
CE = ∆e k = e k − e k− 1 .                           (2) 
 
where k is the time index. 
 
Rule Table and Inference Engine: 
Seven fuzzy linguistic variables are used for the input variables e(k) and ∆e(k). Those are Negative Big 
(NB), Negative Medium (NM), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Medium (PM) and 
Positive Big (PB). The popular shapes are triangular and trapezoidal because these shapes require low 
calculation time. The initial membership functions are illustrated in Fig. 5. 
82                                                S. Ben John Stephen and T. Ruban Devaprakash, 2014 
Advances in Natural and Applied Sciences, 8(20) Special 2014, Pages: 79-84 
 
As observed from the PAM controller, the initial rules are constructed as shown in Table.1. 
 
 
Fig. 5: Initial membership functions 
 
Table 1: Initial Rules 
                E  
CE 
NB NM NS Z PS PM PB 
PB Z PS PM PB PB PB PB 
PM NS Z PS PM PB PB PB 
PS NM NS Z PS PM PB PB 
Z NB NM NS Z PS PM PB 
NS NB NB NM NS Z PS PM 
NM NB NB NB NM NS Z PS 
NB NB NB NB NB NM NS Z 
 
Because the FLC uses the knowledge from the PAM controller, the performance obtaining from the FLC is 
similar to the PAM controller. The efficiency can be improved by adjusting the membership functions and rules. 
The membership functions are adjusted by making the area of membership function near zero region narrower 
to produce finer control resolution. After adjusting the membership functions and rules, the final membership 
functions and rules are obtained as shown in Fig. 6 and Table.2 respectively. 
 
Fig. 6: Final membership functions 
83                                                S. Ben John Stephen and T. Ruban Devaprakash, 2014 
Advances in Natural and Applied Sciences, 8(20) Special 2014, Pages: 79-84 
 
Table 2: Final Rules 
                E 
CE 
NB NM NS Z PS PM PB 
PB NM NS NS NB PB PB PB 
PM NM NM NS NB PB PB PB 
PS NB NM NM Z PB PB PB 
Z NB NB NM Z PM PB PB 
NS NB NB NB Z PM PM PB 
NM NB NB NB NB PS PM PM 
NB NB NB NB NB PS PS PM 
 
Defuzzification: 
In defuzzification process, the linguistic variables are converted into crisp variables (Yousef, H.A., H.M. 
Khalil., 1995). As the center of gravity method is considered to be the best well-known defuzzification method, 
it is utilized in this paper. The formula of this method is 
 
𝑈 =
 𝑆𝑖𝐹𝑖
𝑛
𝑖=1
 𝐹𝑖
𝑛
𝑖=1
.                 (3) 
 
where U is the output from defuzzification, Si is the specific position at i
th
 fuzzy set, and Fi is the 
membership degree at that position. The defuzzified output is the duty cycle d(k). The change in duty cycle 
∆d(k) can be obtained from d(k) and previous duty cycle d(k-1) which is given in Eq. 4. 
 
∆𝑑 𝑘 = 𝑑 𝑘 − 𝑑 𝑘 − 1 .             (4) 
 
Simulation and Results: 
The simulation of Buck-Boost Converter fed DC motor with Fuzzy Logic Controller is done using 
MATLAB/Simulink toolbox. Simulation result is shown in Fig. 7.  
 
Fig. 7: Simulated motor speed characteristics using Fuzzy Logic Controller 
 
Advantages of Fuzzy Logic Control: 
Comparing the motor speed characteristics shown in Fig.4 and Fig.7, it is evident that the settling time of 
speed is 1.3 seconds in Fuzzy Logic Control method and 3 seconds in PAM control method for the same dc 
motor and load conditions. So, the settling time is less in model using Fuzzy Logic Controller. 
 
Conclusion: 
AC-DC Buck-Boost converter employing a single switching device such as IGBT together with a single-
phase diode bridge rectifier is suitable for the control of small capacity dc drives.  
The proposed converter contributes to a reduced number of power switching devices. This may result in 
advantages such as small installation size and less energy loss. One-switch converter is very convenient for an 
economical variable dc voltage supply since the number of switches does not highly affect the characteristics of 
the ac-dc converters. 
The settling time of the speed of motor is less in model using Fuzzy Logic Controller when compared to 
other model using Pulse Area Modulation technique. Thus, the performance of the Fuzzy Logic Controller is 
superior to the conventional control techniques. 
 
84                                                S. Ben John Stephen and T. Ruban Devaprakash, 2014 
Advances in Natural and Applied Sciences, 8(20) Special 2014, Pages: 79-84 
 
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Improved Control Strategy on Buck-Boost Converter Fed DC Motor with Fuzzy Logic Controller

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Improved Control Strategy on Buck-Boost Converter Fed DC Motor with Fuzzy Logic Controller

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