@1a@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ÚÓ‘Ój�n€a@Î@Úœäñ€a@‚Ï‹»‹€@·rÓ:a@Âig@Ú‹©@Ü‹26@@ÖÜ»€a@I1@‚b«@H2013 Ibn Al-Haitham Jour. for Pure & Appl. Sci. Vol. 26 (1) 2013 Control of the AC Induction Motor by Using Fuzzy Logic Aws M. Abdullah University of Baghdad Received in:15 April 2012 , Accepted in:9 December 2012 Abstract The main aim of this research to study and recognize the specifications and main concepts of (Fuzzy Logic) and its components and studying the practical experiments of the (Fuzzy Logic) techniques in the electrical engineering field through by using the (Fuzzy Logic) for controlling the three-phase AC induction motor by using (Matlab_ simulation_7) for modeling the system by using the computer. Keywords : AC Induction motor, Fuzzy Logic, Speed Control. 383 | Computers Science @1a@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ÚÓ‘Ój�n€a@Î@Úœäñ€a@‚Ï‹»‹€@·rÓ:a@Âig@Ú‹©@Ü‹26@@ÖÜ»€a@I1@‚b«@H2013 Ibn Al-Haitham Jour. for Pure & Appl. Sci. Vol. 26 (1) 2013 Introduction The term fuzzy logic was developed in 1965 by Professor Lutffi zadeh. The term is used to describe the groups of multiple values. The concept of multi-valued logic was found in 1920, at University of Heisenberg to deal with quantum mechanics. Lotffi zadeh applied the logic of multi-values and developed the term (fuzzy sets) which is the group that its elements return to different values, and it's considered as transformation from the classical logic which reflects the wrong or right, and the number one or zero to become a fuzzy logic of multi values between zero to one and it is considered as a moving from traditional mathematics and numbers to the philosophical and linguistic mathematics [1]. The concept of fuzzy logic It is a conventional organization based on the popularization the traditional logic of binary values. It is used as an indication to an uncertain conditions. This concept consists of theories and techniques that use the fuzzy sets which are sets without interrupted borders . This logic is a way to describe and represent the human experience; also it offers practical solutions to the problems of realism, with reasonable and effective cost in comparison with the solutions provided by other techniques [1],[2]. Traditional sets and fuzzy sets a) Traditional Set: In the traditional or classical sets, an element can be either belong to a set or does not belong to it at all. For example, Group A and Group U. If we define the function µA, which gives each of the elements of group U the degree of belonging to Group A, by giving it the number 1 i.e. µA (X) = 1. If the element belongs to the group U i.e. element X belongs to Group A. However, if the element X does not belong to A, so the function µA gives number zero i.e. µA (X) = zero. As such, it can be expressed as follows on function µA [2]: µA:U→{ zero, 1} X →µA (X) b) Fuzzy Sets: In the fuzzy group an element can belong to a certain extent for the group. For example, if the group A was the temperatures that are classified as cold (cold for humans) and considers the group U is all temperatures . Let us take the example of the element X = -100, U This temperature is very cold and this exactly belongs to group A i.e. µA (X) = 1. However, if we take a temperature X= +500, this temperature is very warm so the element X dose not belong to A. till now We did not go far from the use of classical logic as A belonged or did not belong. But let's take for example the temperature X =12. In the traditional logic, we have two possibilities either belongs or does not belong A. In the fuzzy logic, we can say that X belongs to a score of 50% to A that is to say temperature 12 is half cold and half moderate for example, A(X) = 0.5. Here we see a difference in the definition of function µA. where it is known mathematically as [3]: µA: U →{ 0,1} X→ µA(X) Where the function can give results between 1,0 on reverse it in the classical logic, where the function gives either number 1 or number 0 [2],[3]. The Operations On The Fuzzy Sets 1_The Contrary: the symbol of the process is - A or À. 2_The Intersection: the symbol of the process is ∩ or Λ. 3_ The Integration: The symbol of the process is U or V. 384 | Computers Science @1a@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ÚÓ‘Ój�n€a@Î@Úœäñ€a@‚Ï‹»‹€@·rÓ:a@Âig@Ú‹©@Ü‹26@@ÖÜ»€a@I1@‚b«@H2013 Ibn Al-Haitham Jour. for Pure & Appl. Sci. Vol. 26 (1) 2013 a) The Contrary: Let us take, for example - A or À. the reverse process of A which is moderate temperatures and B is - A. known as non moderate temperatures, where in the classical logic, for example, moderate temperatures must belong as a whole to A, while at the same time they totally do not belong to B. For example, a temperature is 20 to be subjected to the relationship µA (20) = 1, and at the same time µA (20) = 0. This is an embodiment of the Classical logic, where temperature is 20 either calculated on the moderate group or non moderate. It is not possible to 20 degrees to be at the same time moderate and non moderate. This can be achieved if the membership function is µA=µB [3]. b) The Intersection: The intersection can be defined in the both fuzzy logic and classical logic. as in the case for the contrary process by using mathematical operations on the membership function µ, but in the intersection instead of the use of the subtraction process, we usually use min process. c) The Integration: The integration can be defined in both fuzzy logic and classical logic as in the contrary process. i.e. by using of mathematical operations on the membership function µ, but in the Integration instead of using the subtraction process, we use max process [3],[4]. Some of the terminologies that are used in the context of fuzzy logic given in table (1). The Fuzzy Systems of controlling There are five preliminary components of systems for controlling fuzzy: 1- fuzzy form. 2- base of knowledge. 3- base of laws. 4- information engine. 5- fuzzy opening form. Automatic change in the design of programs for any five elements it will form adaptive fuzzy controller [4]. Fuzzy control system consists of fixed elements, and non-fixed elements are part of the control system which includes conversion sensors from waveform to the digital system and the adapter from conversion from to digital system to waveform system and normalization circuits. There are two types of normalization circuits, the first type is scheduling of an input physical values from controller to natural values. The second type converts the natural values to the physical values[4],[5]. AC induction motor The AC induction motor is the most commonly used type among motors and it is the most famous in the systems of control in the industrial field. In addition to that it is a widespread usage in the main home devices. Also, its design is simple and low cost as compared to other engines and it connects the AC induction motor directly to AC power sources. There are different types of induction motors present in the work market and the difference in these species varies according to the desired application. The speed and torque are tools for controlling multiple types of AC induction motor. All kinds of AC induction motors contain a rotor part and fixed part and use the generated magnetic field to rotate the rotor part [5]. Speed of induction motor The magnetic field generates within the fixed part in asynchronous quick where its symbol is (Ns) and the equation of speed is: Ns = 120 F/P where: Ns: is the synchronous speed in the fixed part. 385 | Computers Science @1a@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ÚÓ‘Ój�n€a@Î@Úœäñ€a@‚Ï‹»‹€@·rÓ:a@Âig@Ú‹©@Ü‹26@@ÖÜ»€a@I1@‚b«@H2013 Ibn Al-Haitham Jour. for Pure & Appl. Sci. Vol. 26 (1) 2013 P: is the number of polars. F: is the frequency of the source. The magnetic field is generated inside the rotor part because of the alternating inductive voltage, where the rotor part rotates in a fixed field at lower speed called (NB), and the difference between the Ns and Nb is called the slip and the last changes according to the equation [5],[6]: slip = ((Ns - Nb) / Ns) * 100 Technology of Matlab_simulink In recent years, the technology of simulink became a broader software packages, from both academic and industrial aspects for modeling and representing of the systems. The benefit of this system or any system similar to it will enable the researcher or user to do the tests that he needs them, so he can build the required forms or taking an older model and do any additions or changes. Here, the process of simulation is an interactive process, so it is possible to change some variables and observing the changing in the results directly on the circuit. Such models are converting the computer to a laboratory for modeling and analyzing of circuits that can not be represented simply in the practical field [7]. We have used in this research the (Matlab_simulink_7) technique in the representation of the three-phase AC induction motor, and it was used technology of fuzzy logic for controlling it, where laws related to fuzzy logic were formulated to deal with the changes of speed and torque in the three-phase AC induction motor, and it can be viewed by clicking on the button of fuzzy logic controller as is shown in the following figure (1) [8]. The laws of fuzzy logic to control The laws of fuzzy logic have been applied to process different situations of expected changes according to the nature of the system, and we will find that the laws are verbal which distinguishes fuzzy logic from other applications: If (slip is VS) then (freqw is VSO). If (slip is S) then (freqw is SO). If (slip is M) then (freqw is MO). If (slip is I) then (freqw is IO). If (slip is VI) then (freqw is VIO) [8],[9]. And these laws can be seen in the program (Matlab_simulink_7) by this path: start _ toolboxes _ fuzzy logic _FIS editor viewer. The results can be shown by pressing button of output which is represented by showing of output curves called (scop), which shows the coordinates of four curves and the coordinates are represented as follows: The First: represents a voltage with time. The second: represents the current with time. The third: represents the torque with time. The fourth: represents the deviation of speed curve. When we press the button of (scop), the following image will appear. Conclusions After studying the previous applications in fuzzy logic technique.We chose the technique of fuzzy logic because the flexibility of this application in the formulation of the laws, but for example, if we used the application of artificial neural network we may need a large number of programming steps or we may need to very complicated calculations in the case of using the application of genetic algorithms. The fuzzy logic controller was applied on the three- phase AC inductive motor which was modulated by the (Matlab_simulink_7). The technique of fuzzy logic is used to deal with uncertain information or fuzzy information through 386 | Computers Science @1a@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ÚÓ‘Ój�n€a@Î@Úœäñ€a@‚Ï‹»‹€@·rÓ:a@Âig@Ú‹©@Ü‹26@@ÖÜ»€a@I1@‚b«@H2013 Ibn Al-Haitham Jour. for Pure & Appl. Sci. Vol. 26 (1) 2013 relations and laws applied by the applicator according to the changes in the work environment (inputs). For this reason, we recommended for using it in the projects that give different situations for particular entrance more than giving numbers or data. References 1. Kaufman,A. and Gupta, M. M. (1985), Introduction to Fuzzy Arithmetic: Theory and Applications, New York: Van Nostrand Reinhold. 2. Cox, E. (1992) Fuzzy Fundamentals, IEEE Spectrum, 58-61. 3. Dubois, D. and Prade, H. (1980) Fuzzy Sets and Systems: Theory and Applications, Orlando, FL: Academic. 4. Maiers J. and Sherif, Y. S. (1985) ,Applications of Fuzzy Set Theory , IEEETrans. Syst., Man, and Cybern., SMC-15, 175-189. 5. Hiyama, T. and Tomsovic, K., (1999) ,Current status of fuzzy system applications in power system, IEEE, smc99, Tokyo, Japan. 527-532. 6. Klir, G. J. and Folger, T. A. (1988) ,Fuzzy Sets, Uncertainty, and Information, Englewood Cliffs, NJ: Prentice Hall. 7. Toliyat, H. A.; Sadeh, J. and Ghazir., (1996) ,Design of Augment Fuzzy Logic Power System Stabilizer to Enhance Power System Stability'', IEEE, 11(1). 8. Mazin, M., Al Hadidi, Yasir Khalil Ibrahim, (2008) ,Car Safety System Using Fuzzy Logic, published in Journal of Computer Science 4(12): 1061-1063. 9. Mazin Al Hadidi, (2007) Utilizing the Fuzzy Logic Model to Develop the Arab Management System", published in the IEEE Computer Society, Las Vegas, ITNG ,959- 961. Table(1): context of fuzzy logic Term Contextual usage Bandwidth Narrowband , broadband Blur Some what , quite , very Correlation Low , medium , high , perfect Errors Large , medium , small , a lot of , not so great ,very large ,very small Frequency High , low , ultra Resolution Low , high Sampling Low rate , medium rate Fig (1): The full form of the system in the program (Matlab_simulink_7) 387 | Computers Science @1a@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ÚÓ‘Ój�n€a@Î@Úœäñ€a@‚Ï‹»‹€@·rÓ:a@Âig@Ú‹©@Ü‹26@@ÖÜ»€a@I1@‚b«@H2013 Ibn Al-Haitham Jour. for Pure & Appl. Sci. Vol. 26 (1) 2013 Fig (2): Explain output curves Fig (3): Represents the membership function for the input signal Fig (4): Shows specifications of the suggested fuzzy logic controller in the system 388 | Computers Science @1a@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ÚÓ‘Ój�n€a@Î@Úœäñ€a@‚Ï‹»‹€@·rÓ:a@Âig@Ú‹©@Ü‹26@@ÖÜ»€a@I1@‚b«@H2013 Ibn Al-Haitham Jour. for Pure & Appl. Sci. Vol. 26 (1) 2013 لحثي المتناوب باستخدام المنطق المضبباالسیطرة على المحرك اوس محمود عبد هللا جامعة بغداد 2012كانون االول 9قبل البحث في ، 2012نیسانفي 15استلم البحث الخالصة Fuzzy)إن الھدف الرئیس من ھذا البحث ھو لدراسة والتعرف على الخصائص والمفاھیم األساسیة للمنطق الضبابي Logic) وأجزائھ، ودراسة التجارب العملیة لتقنیات المنطق الضبابي (Fuzzy Logic) في مجال الھندسة الكھربائیة للقیام بالسیطرة على المحــرك الكـــــھربائي ألحثي الــــمتناوب (Fuzzy Logic) من خالل تطبیق المنطق الضبابي ) للقیام بنمذجة النظام حاسوبیا.Matlab_simulation_7برنامج الــ ( ثــــالثي الـــــطور باســــــــتخدام محرك الحث المتناوب، المنطق المضبب، السیطرة على السرعة. : الكلمات المفتاحیة 389 | Computers Science