Fuzzy Logic Controller (FLC):Application to control DC-DC buck converter K. Bendaoud , S. Krit , M. Kabrane, H. Ouadani, M. Elaskri, K. Karimi, H. Elbousty and L. Elmaimouni
Laboratory of Engineering Sciences and Energies, Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University- Agadir, Morocco,
[email protected],
[email protected],
[email protected] Abstract— Nowadays, DC-DC converters circuits are widely used in electronics systems in order to obtain a stabilized output voltage from a given input DC Voltage. Performance of DC-DC converters is dependent on the choice of control methods. Consequently, controlling of DC-DC converter represents essential task in power conversion. The research problem addressed in this paper is to model a controlling system for buck DC-DC converter. In this way a logic fuzzy controller is used and simulated using MATLAB/SIMULINK to increase converter efficiency and power efficiency. Keywords- DC/DC buck converter,Fuzzy Matlab/Simulink, Controlled converter .
Logic
Controller,
I. INTRODUCTION The DC/DC converter is a device for converting the DC voltage to step-up or step-down depending on the load voltage required. If the requirement of voltage is step-up then it is necessary to use a boost converter. It the requirement of voltage is step-down, and then it necessary to use a buck converter. Sometimes, both step-up and step-down is required to cover the load, but at different times then it is necessary to use a buckboost converter. Therefore, different types of DC DC converters are used for different voltage levels in load [1]. In this way Buck converter has a wide application in electrical industry and power systems and specifically it can supply the voltage for a direct current consumer. Buck is a one-input and multiple-output in structure with a non-linear property due to its switching behavior, but at the same time when the switch is on and off its behavior is linear. Therefore, by employing the averaging method, it is possible to exchange a non-linear system with a linear one [2]. In other word, when a control electronic technique is used in a dc-dc converter, it produces output more efficiently as compared to the converter when used in open loop [3]. Many control methods are used for control of switch mode DC-DC converters and the simple and low cost controller structure is always in demand for most industrial and high performance applications [4]. Several control strategies and mathematical models have been developed by researches. Tobias Geyer , Georgios Papafotiou, and Roberto Frasca [4] have proposed a modeling and control approach for fixed frequency switch mode DC-DC converters by formulatinga constrained optimal control problem using hybrid systems methodologies [4].Ned Mohan, W.P. Robbins, M.Undeland,
R. Nilssen and Olive Mo [5] were among the first to present means of simulating Power Electronics and Motion Control systems [5]. The Concept of Fuzzy Logic system was introduced by Lotfi Zadeh (1965) and its mathematical modeling which is deals with uncertainty [6]. The Fuzzy control represents a practical alternative for a variety of challenging control applications. FLC is nonlinear and adaptive in nature that gives it a robust performance under parameter variation and load disturbances. Fuzzy logic is suited to lowcost implementations and systems of fuzzy can be easily upgraded by adding new rules to improve performance or add new features. In this paper, a brief overview of the operation of a DC-DC Buck Converter is provided, then, an implementation of buck converter using Fuzzy control is illustrated by the tool Matlab/Simulink. II. DC-DC BUCK CONVERTER CIRCUIT MODEL The buck is a popular non-isolated power stage topology, sometimes called a step down power stage. Power supply designers choose the buck power stage because the required output is always less than the input voltage. The basic buck dcdc converter topology consists of a controlled switch S, an uncontrolled switch (diode) D, an inductor L, a capacitor C, and a load resistance R. The input current for a buck power stage is discontinuous, or pulsating. The output current for a buck power stage is continuous or non-pulsating because the output current is supplied by the output inductor/capacitor combination [7]. Figure.1 shows the configuration of a buck converter.
Figure1: Basic configuration of DC-DC buck converter The first state is when the switch is turned on, diode is reverse biased and inductor current flows through the switch, which can be shown in Figure 2. The second state is when the
switch is turned off and current freewheels through the diode, which is shown Figure 3.
system dynamics and the application of these quality ideas simultaneously for power systems [9]. Four major units are fuzzification block, a fuzzy knowledge-base block, a fuzzy inference engine and a defuzzification block [10] as shown in Figure 4.
Figure 2: of DC-DC buck converter when switch is ON Figure 4: Fuzzy logic controller system
The functions of the blocks of the fuzzy system are briefly summarized in below:
Figure 3: DC-DC buck converter when switch is OFF By applying KVL to the first loop of figure.1 we obtain: VIN di (1) L L VOUT D
dt
where D represents the duty cycle D TON / TON TOFF and by applying KCL at the capacitor node we get: C
dVC iL iOUT dt
(2)
Applying Laplace transform to equation (1) we get: VIN D LiL s VOUT (3) Applying Laplace transform to equation (2) and assuming VOUT VC we obtain : iL CVOUT s
VOUT ROUT
(4)
Substituting the value of iL in equation (3) we get: VOUT VIN / LC D s ² 1 / CROUT s 1 / LC
(5)
The fuzzier interface is a block which classify input data into suitable linguistic values or sets ; it measures values of input variables in order to convert them into suitable values that may be viewed as labels of fuzzy. The knowledge base is comprised of two components namely called fuzzy sets (data base) and fuzzy control rule base. The concepts associated with fuzzy sets are used to characterize fuzzy control rules and fuzzy data manipulation in an FLC. These concepts are subjectively defined and based on experience. So, it should be noted that the correct choice of the membership functions of a term set plays an essential role in the success of an application [10]. While fuzzy decision maker represents the kernel of a fuzzy logic controller, it has capability of simulating human decisionmaking based on fuzzy concepts and of inferring fuzzy control actions using fuzzy implication (fuzzy relation) and the rules of inference in fuzzy logic. This means that the fuzzy decision maker handles rule inference where human experience can easily be injected through linguistic rules. Finally, defuzzier interface converts the conclusion from the fuzzy logical decision maker into the control input to the plant. IV. MODELING OF FUZZY LOGIC CONTROLLER FOR BUCK CONVERTER USING MATLAB/SIMULINK
where VIN , VOUT and ROUT are respectively the input voltage, output voltage and load resistane. Equation (5) reprensents the open loop transfer function of the buck converter
The intend of this part is to model DC-DC buck converter and fuzzy logic controller (FLC) which improve efficiency and performance of the converter. Modelling and simulation are performed on Matlab/Simulink.
III. FUZZY LOGIC CONTROLLER (FLC)
A. Buck converter modeling using Matlab/Simulink Referring to the equation (5), the buck converter can be modelled as a transfer function to permit automatic tuning as shown in Figure 5.
Fuzzy controllers fulfill the same tasks like a classic controller. Fuzzy controller differs from the classic one in that it manages the control complex space in a heuristic manner. However, the fuzzy controller can approximate, with any precision level, linear and non-linear control functions. In many situations is easier to obtain a fuzzy controller with high performance than a classic controller with same performances [8]. The most important specifications of fuzzy control method are their fuzzy logical ability in the quality perception of
Figure 5:Transfer function model of a buck converter
B. Fuzzy logic controller modeling using Matlab/Simulink The derivation of the fuzzy control rules is based on human knowledge about what to do if system output get increase or decrease and based on the following criteria [11]: When the converter output is not reference value and it far from reference point, duty cycle should be large to bring the output to the reference point rapidly. When the converter output is less than desired output voltage then duty cycle should be increased to get target output. When converter output is greater than desired output voltage then duty cycle should be decreased to get target output. The input of a FLC are respectively Error which is is the difference between the reference voltage and the output voltage of the converter sampled at the same time, and Change in Error which represents the difference between current and previous error. Then, it is necessary to determine membership functions assigned to inputs and outputs as shown in Table 1. Figure 6 illustrates membership function of fuzzy logic controller using FUZZY toolbox in Simulink. Table 1 : Fuzzy contro rules
Figure 7: Simulink model of buck converter with FLC A simulation run for the Buck Converter is carried out whose design parameters are specified in Table 2. Table 2 : Design parameters Parameters Input voltage Output voltage Load resistance Frequence Inductance Capacitance
Values 12 V 5V 500Ω 50 KHZ 200 µH 200 µF
Waveforms of buck converter output voltage in open loop mode are shown in Figure 8.
Error Change In Error
PS
PB
ZE
NS
NB
PB PS ZE NS NB
PB PB PS ZE NS
PB PB PB PS ZE
PB PS ZE NS NB
PS ZE NS NB NB
ZE NS NB NB NB
Figure 8:output voltage in open loop mode The simulation results of output voltage for buck converter with fuzzy logic controller have shown at following figures , the value of output voltage is getting about 5V. Figure 8 and Figure 9 show results of fuzzy controlled buck converter for supply voltage 12V and load variant condition. Figure 11 and Figure 12 show results for 500 ohm load and input supply variant voltage. Figure 6 : Membreship function of FLC using Simulink V.SIMULATION RESULTS A Matlab/Simulink model of DC-DC buck converter controlled with a fuzzy logic controller is shown in Figure7.
The fuzzy logic controller provides a method to control the buck converter in order to be insensitive to parameter variations and external load disturbances; by changing load values and supply voltage values we get constant and stable output voltage of buck converter. Briefly, the fuzzy logic controller is a efficient controller for buck converter.
Figure 9: Output voltage of fuzzy controlled buck Converter for 5 ohm load
Figure 10: Output voltage of fuzzy controlled buck converter for 500 ohm load
CONCLUSION In this paper, we provided a brief overview of buck DCDC converter and fuzzy logic controller, and proceeded to illustrate a model of fuzzy logic controlled buck converter using Matlab/Simulink. This paper demonstrated the effectiveness of fuzzy controller applied to DC-DC buck converter. Good behavior of the buck converter proves the robustness of the controller especially it shows good stabilization quality. ACKNOWLEDGMENT: Kaoutar Bendaoud & all are gratefully acknowledges the support by the National Centre for Scientific and Technical Research (CNRST) of Morroco. REFERENCES [1]
[2] [3]
Figure 11:output voltage of fuzzy controlled buck converter for 12V supply voltage
[4]
[5]
[6]
[7]
Figure 12: Output voltage of fuzzy controlled buck converter for 25V supply voltage In open loop mode , output voltage is sensitive to parameters variations (Figure 8) .Results of fuzzy controlled buck converter for 12 V supply voltage and load variant conditions show that output voltage is 5.5 V for 5 Ohm load (Figure 9) and is 4.9 V for 500 Ohm load (Figure 10) .While results of fuzzy controlled buck converter for 500 Ohm load and variant supply voltage show 5.04 V for 12 V supply voltage (Figure 11) and 5.8V for 25V supply voltage (Figure 12).
[8]
[9] [10] [11]
H. Pinheiro, A. Martins, and J. Pinheiro, "Single-phase voltage inverters controlled by sliding mode", Proc. Brazilian Automatic Control Conf. (CBA',94), pp.1177 -1182 Kostov, K. S., Kyyra, J., Suntio, T., The input impedance of a buck converter European power Electronics Conf 2003 S. Vigneshwaran ,R. Vijayalakshmi High Efficiency DC/DC Buck-Boost Converters , American-Eurasian Journal of Scientific Research 11 Tobias Geyer, GeorgiosPapafotiou and Manfred Morari, Constrained Optimal Control of the Step-Down DC–DC Converter IEEE Transactions on Power Electronics, Vol. 23, No. 5, September 2008 Ned Mohan, W.P. Robbins and T.M.Undeland, R. Nilssen and Olive Mo: Simulation of Power Electronic and Motion Control System Proceedings of the IEEE, Vol.82, No.8, August 1994, pp. 1287 – 1292 Paolo Mattavelli, L.R., Giorgio Spiazzi and Paolo Tenti, 1997 . General-Purpose Fuzzy Controller for DC-DC converters IEEE Trans, 12(1): 79-86. Puspendu Maji1, Prof. G K. Panda2, Prof. P K. Saha, Performance Analysis for DC-DC Buck Converter with Fuzzy and Robust Controller , International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering , Vol. 4, Issue 5, May 2015 Eremia M., Cartina G., Petrica D., s.a. Artificial intelligence techniques in the management of power systems. Editura Agir, Bucuresti, 2006. Terano, T., AsaI, K., Sugeno, M.,applied fuzzy systems, Academic Presss Inc., pp.86-93, 1994. Lee, C. C., Fuzzy Logic in Control Systems :Fuzzy logic controller Part I, IEEE Trans. On Syst. Man Cybern., vol. 20. K.V.H. Prasad, CH.U. M. Rao, A.S. Hari, ―Design and simulation of a fuzzy Logic Controller for Buck & Boost Converters‖, nternational Journal of Advanced Technology & Engineering Research , May 2012,Vol. 2, Issue3, pp.218-224.
Kaoutar Bendaoud received the Engineer Degree in Electronics and Automatics Engineering from the National School of Applied Science of Tangier, Morocco in 2015. She is currently a Phd candidate at the Polydisciplinary Faculty of Ouarzazate , Ibn Zohr University, Morocco. Her research interesets include linear regulators, DC-DC power converters, switching converters for mobile applications. Salah-ddine Krit received the B.S. and Ph.D degrees in Microectronics Engineering from Sidi Mohammed Ben Abdellah university, Fez, Morroco. Institute in 2004 and 2009, respectively. During 2002-2008, he is also an engineer Team leader in audio and power management Integrated Circuits (ICs) Research. Design, simulation and layout of analog and digital blocks dedicated for mobile phone and satellite communication systems using CMOStechnology.He is currently a professor of informatics- Physics with Polydisciplinary Faculty of Ouarzazate, Ibn Zohr university, Agadir, Morroco. His research interests include wireless sensor Networks (Software and Hardware), computer engineering and wireless communications. Lahoucine EL MAIMOUNI was born in Zagora, Morocco, in 1970. He received in 2005, the Ph.D. degree in electronics from Institute of Electronics, Microelectronics and Nanotechnology (IEMN) University of Valenciennes, France. In 2006, he joined the Polydisciplinary Faculty of ouarzazate, Ibn Zohr University, Morocco. In 2011, he received his Habilitation à Diriger des Recherches (HDR) from the Faculty of sciences, Ibn Zohr University, Agadir. At present, his research activities are focused on acoustic wave propagation in piezoelectric structures, BAW resonators, piezoelectric sensor, acoustic wave resonators and filters for RF-MEMS, and audiovisual techniques for image and sound. Mustapha Kabrane received his the first Master’s degree in Electronics, Automatics and Computer, from the Faculty of Sciences, University of Perpignan Via Domitia , France, in 2012, and his the second Master’s degree in Computer Sciences from Institute of Sciences and Technology, University of Valenciennes, France, In 2013. He is currently a PhD student. His research interests include wireless sensor Networks implemented in the management and control
of urban traffic at the Polydisciplinary Faculty of Ouarzazate, Ibn Zohr university, Agadir, Morroco. Mohamed El Asikri received the Engineer Degree in Computer Science from IbnoZohr University in 2010 from ENSA Agadir, Morroco. He is also a networks and systems administrator engineer. He is currently a PhD student in Polydisciplinary Faculty of Ouarzazate, Department Mathematics and Informatics and Management, Laboratory of Engineering Sciences and Energy, Ibn Zohr University- Agadir. His research interests Knoledge discovery and Web Semantic Mining. Khaoula KARIMI received the Engineer Degree In Software engineering from Faculty of Sciences and Technologies, Settat, Morocco, In 2015. She is currently a PhD student in Polydisciplinary. Faculty of Ouarzazate, Department Mathematics, and Informatics and Management, Ibn Zohr University Agadir, Morocco. Her research interests design and implementation of a Smart-home/Smartphone. Hicham EL BOUSTY is currently a PhD student atthe Laboratory of Engineering Sciences and Energy, Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University, Morocco. He obtained his engineering degree in computer science in 2009, from the National School of Mineral Industry, Rabat, Morocco. Business intelligence and analytics are the main fields of his research. In addition to his academic career, he is responsible of the security of the Moroccan identification system.
