# INTRODUCTION lassic Control has proven for a long time to be good enough to handle control tasks on system control; however his implementation relies on an exact mathematical model of the plan to be controller and not simple mathematical operations. The fuzzy logic, unlike conventional logic system, is able to model inaccurate or imprecise models. The fuzzy logic approach offers a simpler, quicker and more reliable solution that is clear advantages over conventional techniques. Fuzzy logic may be viewed as form of set theory. At the present time, MATLAB Simulation simplifies the scientific computation, process control, research, industrial application and measurement applications. Because MATLAB has the flexibility of a programming language combined with built-in tools designed specifically for test, measurement and control. By using the integrated MATLAB environment to interface with real-world signals, analyze data for meaningful information and share results. Therefore take MATLAB for develop of the control system that append with fuzzy logic is incoming for modem control and the advantages in fuzzy control are more robust control method than usual conventional control to variation of system parameter. This paper presents the experimental results of the fuzzy logic controller using Matlab for speed control of Separately Excited DC Motor through fuzzy logic controller for speed control is used to facilitate and efficiency the implementation of controllers. # II. # SYSTEM DESCRIPTION a) Motor Model The resistance of the field winding and its inductance of the motor used in this study are represented by R f and L a respectively in dynamic model. Armature reactions effects are ignored in the description of the motor. This negligence is justifiable to minimize the effects of armature reaction since the motor used has either interpoles or compensating winding. The fixed voltage V f is applied to the field and the field current settles down to a constant value. A linear model of a simple DC motor consists of a mechanical equation and electrical equation as determined in the following equations ( 1) - (2). T m = J m d?/dt +B m ?+T L (1) V a =E b + I a R a + L a (dI a /dt)(2) Where V a is the armature voltage. (In volt) E b is back emf the motor (In volt) I a is the armature current (In ampere) R a is the armature resistance (In ohm) L a is the armature inductance (In henry) T m is the mechanical torque developed (In Nm) J m is moment of inertia (In kg/m²) B m is friction coefficient of the motor (In Nm/ (rad/sec)) ? is angular velocity (In rad/sec) The dynamic model of the system is formed using these differential equations. # FUZZY LOGIC CONTROLLER Fuzzy logic is a method of rule-based decision making used for expert systems and process control that emulates the rule-of-thumb thought process used by human beings. The basis of fuzzy logic is fuzzy set theory which was developed by Lotfi Zadeh in the 1960s. Fuzzy set theory differs from traditional Boolean (or two-valued) set theory in that partial membership in a set is allowed. Traditional Boolean set theory is twovalued in the sense that a member belongs to a set or does not and is represented by 1 or 0, respectively. Fuzzy set theory allows for partial membership or a degree of membership, which might be any value along the continuum of 0 to 1. A linguistic term can be defined quantitatively by a type of fuzzy set known as a membership function. The membership function specifically defines degrees of membership based on a property such as temperature or pressure. With membership functions defined for controller or expert system inputs and outputs, the formulation of a rule base of IF-THEN type conditional rules is done. Such a rule base and the corresponding membership functions are employed to analyze controller inputs and determine controller outputs by the process of fuzzy logic inference. By defining such a fuzzy controller, process control can be implemented quickly and easily. Many such systems are difficult or impossible to model mathematically, which is required for the design of most traditional control algorithms. In addition, many processes that might or might not be modeled mathematically are too complex or nonlinear to be controlled with traditional strategies. However, if a control strategy can be described qualitatively by an expert, fuzzy logic can be used to define a controller that emulates the heuristic rule-of-thumb strategies of the expert. Therefore, fuzzy logic can be used to control a process that a human can control manually with expertise gained from experience. The linguistic control rules that a human expert can describe in an intuitive and general manner can be directly translated to a rule base for a fuzzy logic controller. # Problem Formulation A Separately Excited DC motor is taken as a case study and the control is achieved using intelligent fuzzy logic based controller. The efficiency is improved by controlling the speed with fuzzy logic controller and results are shown graphically. # a) Fuzzy Logic Controller Design The inputs to the Self-tuning Fuzzy Controller are speed error "e (t)" and Change-in-speed error "de (t)". The input shown in figure are described by e (t)=wr (t)-wa (t) de (t)=e(t)-e(t-1) Using fuzzy control rules the output control is adjusted, which constitute the self control of D.C. machine. # V. ADJUSTING FUZZY MEMBERSHIP FUNCTIONS AND RULES In order to improve the performance of FLC, the rules and membership functions are adjusted. The membership functions are adjusted by making the area of membership functions near ZE region narrower to produce finer control resolution. On the other hand, making the area far from ZE region wider gives faster control response. Also the performance can be improved by changing the severity of rules. # CONCLUSION This paper proposes a straight-forward method of creating a mathematical model which has been successfully applied to a variety of membership functions. This new approach offers a key of advantage over the traditional methods, which makes it suitable for several dc motor drive applications. 12![Figure 1 : Separately Excited DC Motor Model](image-2.png "Figure 1 :Figure 2 :") 3![Figure 3 : Structure of fuzzy logic controller IV.](image-3.png "Figure 3 :") 43)712![Figure 4 : Rule bases for output control](image-4.png "Figure 4 : 3 ( ) YearFigure 7 :Figure 12 :") 14![Figure 14 : Surface view for controls VII.](image-5.png "Figure 14 :") ![The paper focused the attention to apply the smooth control of speed in D.C. Machines up to the 95% and with minimization of speed error. The simulation and experimental studies clearly indicate the superior of fuzzy control. It is well seen in the case of sudden change due to load torque disturbances because it is inherently adaptive in nature. The final experimental results clarify the success, the simplicity and the generality of the design software controller. The extension of this research is to apply the neural network techniques for the dc motor applications. obal Journal of Researches in Engineering Gl © 2013 Global Journals Inc. (US) 5. Ahmed, F.I.; Mahfouz, A.A.; Ibrahim, M.M.; "A novel fuzzy controller for DC motor drives," Electrical](image-6.png "F") ![](image-7.png "") ![](image-8.png "") ![](image-9.png "") ![](image-10.png "") 1a) Design of Membership Function (MF)i. Input Variablesa. Fuzzy Sets of Speed Error (E) VariableFuzzy Set Error Numerical RangeShape of membership functionVery Low0.2 to 0.5 1 to 1TrapezoidalInstant-0.01 to 0 0 to 0.01Triangular 3OutputNumerical RangeShape of member-ship functionDecrease A lot-30 to -25 -25 to -20TriangularIncrease A lot20 to 25 25 to 30TriangularDecrease Few-15 to -10 -10 to -5TriangularHold-0.1 to 0 0 to 0.1]TriangularIncrease Few5 to 10 10 to 15Triangularc) Design of Fuzzy Rulesi.Rule bases for Output Controle/deVery HighMedium HighInstantMedium LowVery LowHigh NegativeDecrease alotDecrease fewDecrease fewIncrease fewIncrease alotHigh positiveDecrease alotDecrease fewIncrease fewIncrease fewIncrease alotHold Year© 2013 Global Journals Inc. (US) XIII Issue X Version I 2 ( ) © 2013 Global Journals Inc. (US) © 2013 Global Journals Inc. (US) High Performance Fuzzy Adaptive Control For D.C. Motor * Fuzzy logic in control systems: fuzzy logic controller. I," Systems, Man and Cybernetics CCLee IEEE Transactions on 20 2 Mar/Apr 1990 * MalhotraRahul * DC Motor control using fuzzy logic controller IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES 8 Kaur Tejbeer * A fuzzy set theory based control of a phase-controlled converter DC machine drive GC DSousa BKBose Industry Applications Jan/Feb 1994 30 * An experiment in automatic modeling an electrical drive system using fuzzy logic CostaBranco PJDente JA Systems, Man, and Cybernetics, Part C: Applications and Reviews 28 2 May 1998 IEEE Transactions on