A FUZZY BASED SEPERATELY EXCITED DC MOTOR

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1 A UY BASED SEPERATELY EXCTED DC MOTOR G.R.S NAGA KUMAR 1, DANDUPAT SATYANARAYANA 2, REDDY VJAYKUMA 3 1 Assistant Professor, Electrical Department, KL University, Guntur, ndia 2, 3 Student, Electrical Department, KL University, Guntur, ndia ABSTRACT The main aim of this paper is to propose the application of PD and uzzy controllers for speed control of separately excited DC motor. n this paper an SEM dc motor is implemented using transfer function analysis. The transfer function input and output parameters are chosen as armature voltage and speed (rad/sec). Matlab/Simulink is used for testing this uzzy based SEM Dc motor. inally, the result shows that the uzzy approach has minimum overshoot, minimum transient and steady state parameters, which shows more effectiveness and efficiency of uzzy than conventional PD. Keywords: DC Motor, uzzy Control, & PD. NTRODUCTON The advancement of elite motor drives is essential in modern and also other reason applications, for example, steel moving factories, electric trains and mechanical technology. By and large, a superior motor drive framework must have great element speed summon following and load managing reaction to perform errand. DC drives, as a result of their straightforwardness, simplicity of use, high reliabilities, adaptabilities and ideal cost have for some time been a spine of modern applications, robot controllers and home machines where speed and position control of motor are required. DC drives are less unpredictable with a solitary power transformation from AC to DC. Again the speed torque attributes of DC motors are significantly better than that of AC motors. A DC motors give fabulous control of speed to quickening and deceleration. DC drives are typically less costly for most torque appraisals. DC motors have a long convention of utilization as customizable speed machines and an extensive variety of alternatives have developed for this reason. n these applications, the motor ought to be absolutely controlled to give the coveted execution. The controllers of the speed that are considered for objective to control the speed of DC motor to execute one assortment of assignments, is of a few traditional and numeric controller sorts, the controllers can be: corresponding indispensable (P), relative necessary subordinate (PD) uzzy Logic Controller (LC) or the mix between them: uzzy-neural Networks, uzzy-genetic Algorithm, uzzy-ants Colony, uzzy-swarm. The relative vital subordinate (PD) controller works most of the control framework on the planet. t has been accounted for that over 95% of the controllers in the modern procedure control applications are of PD sort as no other controller coordinate the straightforwardness, clear usefulness, relevance and convenience offered by the PD controller [3], [4]. PD controllers give vigorous and solid execution to most frameworks if the PD parameters are tuned appropriately. SYSTEM MODELNG O SEPARATELY EXCTED DC MOTOR: The equivalent circuit for an independently energized dc motor is appeared in igure 1. At the point when an independently energized motor is energized by a field current if and armature current ia streams in the armature 492

2 circuit, the motor builds up a back emf and a torque to adjust the heap torque at a specific speed. The field current of an independently energized motor is autonomous of the armature current ia and any adjustment in the armature current has no impact on the field current. The field current is ordinarily a great deal not as much as the armature current. a(s) = (Va Eb)/ (Ra + LaS) Now, taking equation (ii) into consideration, we have: a(s) = (Va KΦω)/ Ra (1+ LaS/Ra) And ω(s) = (Tm - TL) /JS = (KΦa - TL) /JmS (Armature Time Constant) Ta= La/Ra igure 2: DC Motor Equivalent Circuit igure 1: DC Motor Equivalent Circuit Modeling Of Separately Excited Dc Motor: rom figure 1: The armature voltage equation is given by: Va =Eb+ ara+ La (da/dt) Now the torque balance equation will be given by: Tm = Jm(dω/dt) +Bm(ω)+TL Where: TL is load torque in Nm. riction in rotor of motor is very small (can be neglected), so Bm= 0 Therefore, new torque balance equation will be given by: Tm = Jm(dω/dt) + TL (i) Taking field flux as Φ and Back EM Constant as K. Equation for back emf of motor will be: Eb = K Φ ω (ii) Also, Tm = K Φ a (iii) Taking laplace transform of the motor s armature voltage equation we get After simplifying the above motor model, the overall transfer function will be ω (s) / Va(s) = [KΦ /Ra] /JmS(1+TaS) /[ 1 +(K²Φ² /Ra) /JmS(1+TaS)] P Controller A P Controller (proportional-integral controller) is a combination of proportional and integral controller which is used for eliminating steady state error and peak overshoots The absence of derivative controller shows more stability under noise conditions. This is because the derivative controller is more sensitive under high frequency systems. The general expression for P controller is expressed as, K P K dt E. uzzy Logic Controller n the previous section, control strategy based on P controller is discussed. But in case of P controller, it has high settling time and has large steady state error. n order to rectify this problem, this paper proposes the application of a fuzzy controller shown in igure 3. Generally, the LC 12 is one of the most important software based technique in adaptive methods. As compared with previous controllers, the LC has low settling time, low steady state errors. 493

3 independently energized dc engine is utilized as a e(t) K1 d/dt K2 U C A T O N RULE BASE NERENCE MECHANSM D E U C A T O N K3 u(t) system and discover the reaction of the system applying the progression work as an info. igure 3: basic structure of fuzzy logic controller The error which is obtained from the comparison of reference and actual values is given to fuzzy inference engine. The input variables such as error and error rate are expressed in terms of fuzzy set with the linguistic terms {el, em, eh} and Pin this type of mamdani fuzzy inference system the linguistic terms are expressed using triangular membership functions. n this paper, two inputs and single output fuzzy inference system is considered. The second input is chosen as rate of change of error. The number of linguistic variables for input and output is assumed as 3. The numbers of rules are formed as 9. igure 5: Simulation Diagram for DC Motor with PD & uzzy Controller igure 6: Simulation Result for Speed of DC Motor using PD controller igure 4: Rule-Base formation S system The fuzzy rules are obtained with if-then statements. The given fuzzy inference system is a combination of single input and single output. This input is related with the logical operator AND i.e minimum. SMULATON RESULTS: The results of the system with utilizing different of controllers are appeared here. The reactions of the system with a few controllers, for example, PD, uzzy Logic Controller are being connected. n this area exchange capacity of the igure 7: Simulation Result for Speed with uzzy Controllers 494

4 and Technology (JETT) - Volume4 ssue6- June 2013 igure 8: Simulation Result for Speed with PD and uzzy Controllers Time PD UY Peak Time Rise Time Delay Time Settling Time Peak Overshoot 46 5 Table 1: Comparison of Time domain specifications Between PD & uzzy Controllers CONCLUSON uzzy and PD two different controllers are proposed in this paper for controlling speed of dc motor. The proposed dc motor is tested using Matlab/Simulink using transfer function analysis with both PD and uzzy controllers. rom the results we conclude that with the help of uzzy controller the systems peak-over shoot, settling time, oscillation damping and other time domain specifications are improved as compared with conventional PD controller. The proposed uzzy controller has more advantages, such as higher flexibility, control, better dynamic and static performance compared with conventional controller. REERENCES 1. Speed Control of Separately Excited Dc Motor Using uzzy Logic Controller, Rekha kushwah,, Sulochana Wadhwani,, nternational Journal of Engineering Trends 2. Manafeddin Namazov, DC motor position control using fuzzy proportional-derivative controllers with different Defuzzification methods An Official Journal of Turkish uzzy Systems Association Vol.1, No.1, pp , Vikas S. Wadnerkar, Mithun M. Bhaskar, Tulasi Ram Das and A.D. Raj Kumar, A New uzzy Logic based Modelling and Simulation of a Switched Reluctance Motor, Journal of Electrical Engineering & Technology Vol. 5, No. 2, pp , Atul Kumar Dewangan, Sashai Shukla, Vinod Yadu Speed Control of a Separately Excited DC Motor Using uzzy Logic Control Based on Matlab Simulation Program nternational Journal of Scientific & Technology Research Volume 1, ssue 2, SSN pp ,March adeh L. A., "Outline of a New Approach to the Analysis of Complex Systems and Decision Processes", EEE Transactions Systems, Man and Cybernetics, SMC-3, 1973, pp Sulochana Wadhwani, Veena Verma, Rekha Kushwah, Design and tuning of PD controller parameters based on fuzzy logic and genetic algorithm, nt. Conf. On soft computing, artificial intelligence, pattern recognition, biomedical engineering and associated technologies (SAPBEATS), eb 23-24,2013, department of electrical engineering, MBM eng. College Jai Narain Vyas university, jodhpur. 7. Nader Jamali Sufi Amlashi Design and mplementation of uzzy Position Control 495

5 System for Tracking Applications and Performance Comparison with Conventional PD AES nternational Journal of Artificial ntelligence (J-A) Vol. 1, No. 1, March 2012, pp SSN: eyad Assi Obaid, Member, AENG, Nasri Sulaiman, M. H. Marhaban And M. N. Hamidon, Member AENG Analysis and Performance Evaluation of PDlike uzzy Logic Controller Design Based on Matlab and PGA AENG nternational Journal of Computer Science, 37:2, JCS_37_2_04 (Advance online publication: 13 May 2010). 9. Rekha Kushwah, Sulochana Wadhwani, uzzy Logic based Tuning of PD Controller Parameters, National Conference on New rontiers for Women in Science and Technology March 20-21, 2013, Jiwaji University, Gwalior (M.P.). 496