Published on

Published in: Technology, Business
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide


  1. 1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 6, June (2014), pp. 44-48 © IAEME 44 SIMULATION OF SRM USING FUZZY LOGIC 1 Kiran Srivastava, 2 B.K. Singh 1 RKGIT, Ghaziabad, India 2 Kumaon Engineering College, Dwarhat India ABSTRACT This paper presents the use of fuzzy logic for switched reluctance motor (SRM) speed. The (Fuzzy Logic Control) FLC performs a PI-like control strategy, giving the current reference variation based on speed error and its change. The performance of the drive system was evaluated through digital simulations through the toolbox Simulink/ Matlab program Fuzzy controller and fuzzy logic are generally non-linear systems; hence they can provide better performance in this case. Fuzzy controller is mostly presented as a direct fuzzy controller or as a system, which realizes continued changing parameters of other controller and the decision form of the fuzzy control is illustrated and simulated. Key words: Switched Reluctance Motor, Fuzzy Logic Controller, Simulation. INTRODUCTION The switched reluctance motor (SRM) has becoming an attractive alternative in variable speed drives, due to its advantages such as structural simplicity, high reliability and low cost [1,2]. Many papers have been written about SRM concerning design and control [3]. An important characteristic of the SRM is that the inductance of the magnetic circuit is a nonlinear function of the phase current and rotor position. So, for the control and optimization of this drive, a precise magnetic model is necessary. To obtain this model is not an easy task, because the magnetic circuit operates at varying levels of saturation under operating conditions [4]. Further, the nonlinear characteristic of this plant represents a challenge to classical control. To overcome this drawback, some alternatives have been suggested in [5], using fuzzy and neuronal systems. A PI Controller (proportional-integral controller) is a special case of the PID controller in which the derivative of the error is not used. Fuzzy logic controller is an intelligent controller which uses fuzzy logic to process the input. Fuzzy logic is a many valued logic which is much like human reasoning. INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY (IJEET) ISSN 0976 – 6545(Print) ISSN 0976 – 6553(Online) Volume 5, Issue 6, June (2014), pp. 44-48 © IAEME: www.iaeme.com/ijeet.asp Journal Impact Factor (2014): 6.8310 (Calculated by GISI) www.jifactor.com IJEET © I A E M E
  2. 2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 6, June (2014), pp. 44-48 © IAEME 45 In this paper we present a study by simulation of the use of a FLC for SR drive. The SRM simulated has a structure of eight poles on the stator and six on the rotor. The objective of the FLC is to present a good performance. STRUCTURE OF SRM The Switched Reluctance Motor has gained significant interest in the field of industrial drive. It has numerous advantages like simple and robust construction, reliability, low manufacturing cost, high starting torque, high efficiency, and high speed capacity. The stator has concentrated windings wound field coils and the rotor has no coils or magnets. The stator and rotor have salient poles; hence, the machine is a doubly salient machine. Switched Reluctance Motor is a highly nonlinear control plant and operates in saturation to maximize the torque output. The principle of operation is such that the motion is produced as a result of variable reluctance in the air gap between the rotor and the stator. When the voltage is applied to the stator phase, the rotor tries to rotate in the direction of minimum reluctance position producing reluctance torque. In order to achieve a full rotation of the motor, the windings must be energized in the correct sequence. The Switched Reluctance Motor operates in all the four quadrants and it is suitable to operate in hazardous areas also [7]. Fig. 1: Structure of 4 phase 8/6 SRM The voltage equation for SRM is given by, V= r i +dΨ / dt , ψ=Li=Nφ …… (1) For r = 0 V = L di/dt + i (dL /dθ) (dθ/dt)….. (2) V = L di/dt + i ω (dL/dθ) …………. (3) T = ½ i2 dL/dӨ…………………….. (4) Where V is voltage, L is Inductance, r is resistance in winding, θ is rotor position, Ψ is flux linkages, i current in each phase. This equation shows that the torque developed depends only on the
  3. 3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 6, June (2014), pp. 44-48 © IAEME 46 magnitude of current & direction of dL/dӨ but independent of direction of current [8]. MOTOR SIMULATION Fig. 2 shows a simulation diagram in Matlab- Simulink. In the simulation is thought SRM 8/6 making use for modeling a non-linearity called look-up table, which relatively truly matches a nonlinear system. The fuzzy logic makes the parameter change on the basis of input current and mutual position of rotor and stator pole. This is control method of SRM with current controller. There are also included blocks in the regulation structure for determination the conduction of individual motor phases [9], [10]. Fig. 2: SRM control structure in Matlab-Simulink FUZZY LOGIC CONTROLLER It is good to remind for introduction that the general logics was developed for the change of parameters of PID controller. The fuzzy logics was used for the creating PID controller with nonlinear setting of parameters K, TI, a TD for the reducing overshoot or acceleration of transient effect. In this case the fuzzy controllers evaluates values of input. The value of current PI controller parameters is changed according to set the rule base and function of pertinence in every step. The rule of fuzzy logic may be in form: IF current is small AND position is high THEN output is small The same results can be obtained, if you use the fuzzy PI controller with nonlinear setting. Until you know the setting of PI controller parameters for an environment of the operating points in which regulation system is. It can be selected the correct setting of controller parameters with help of fuzzy supervisor. There is not to think only one complexion fuzzy PI controller due to rising severity in these simulation cases and from practical overview. The fuzzy controller is with two inputs and division ‘universe’ on 7 functions to needs 49 rules. When you have the same number of division universe and you want to rise up the number of inputs for good description of nonlinear system with 4 inputs then the number of rule rise up to 2401. On this account it is important to combine more fuzzy structures with inputs less than one fuzzy system with huge number of rule [11]. We can describe fuzzy system by next equation:
  4. 4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 6, June (2014), pp. 44-48 © IAEME 47 output = D { interference{ F(input_current), F( input_position)} } where F is representing fuzzification, D defuzzification. It was selected the variation which is observing the same PI controller which is using superior adaptation fuzzy controller for the change of parameters. The inner structure of adaptation block from Fig. 2, we can see in Fig. 3. It is clear, that adaptation is performed in certain range of input values which have the influence for motion of SRM. The outputs of block are signals corresponding to gain and time constant for classical PI controller. Fig. 3: Scheme of fuzzy controller for parameters setting The fuzzy controller which has two inputs and one output too. We can set its nonlinear behave with the aid of rule base. It is expert system, where the rule base entry is on the foundation of knowledge and experience of an expert with system. The control surface is result of designed fuzzy system. The table 1 shows the rule data base SIMULATION RESULT Designed adaptation controller for parameters setting was verified on described mathematic model. The courses introduced below in figures are achieved for changing applied current value from 30A to 50A. There are showed the phase current courses with corresponding logic signal value which corresponds to leading specific phase applied time. Fig.4: Phase current Ia time courses Fig 5: Phase current Ia time courses with Iref = 30A with Iref = 50A
  5. 5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 6, June (2014), pp. 44-48 © IAEME 48 CONCLUSION The result of designed system of current control with classic PI controller, which is observed by fuzzy supervisor, is improved current courses during changing system parameters. The main output of designed fuzzy supervisor for simple review of system non-linearity is so-called control area which determines non-linearity of the system. Current control independence during changing system parameters is shown in figures 4 and 5 of current courses. The independence is given by the change of the PI controller parameters. As it was mentioned before, the main advantage of fuzzy control is a possibility to create for the drive suitable control on basic rules. We can achieve better control results because of the fuzzy systems non-linearity. It is confirmed by these simulation results. REFERENCES [1] T.J.E.Miller “Switched Reluctance Motor and their control.”Magna Physics Publishing and Claredon Press-Oxford, 1993. [2] Le- Huy,’ Switched Reluctance Motor Drive: A survey ‘”Seminano Inernacional de MotoresElectricos e acionamentos Regulaveis Proceeding Sao Paulo, Brazil. pp -221-138, May,1991. [3] J.J.Gribble, P.C.Kjaer, C.Cossar, T.J.E.Miller. “Optical commutation angles for current control Switched Reluctance Motor,” power Electronics and Variable Speed Drives Conference Publication No. 429, IEE, pp87-91, September 1996. [4] C.Elmas, s.Sargiroglu, I colak, G.Bal, “Modelling of switched Reluctance drive based on artificial neural networks”. Power Electronics and Variable Speed Drives Conference Proceedings, pp 7-12, 1994. [5] D.S.Reay. M.M Moud, T.C.Green, B.W.Williams Switched Reluctance Motor control via fuzzy adaptive systems”. IEEE Control system, June 1995. [6] J.M.Mendel. “Fuzzy Logic system for engineering: Tutorial Proceedings of the IEEE vol. 83, no.3,March,1995. [7] S. Vijayan, S. Paramasivam, R. Arumugam, S. S. Dash, K. J. Poornaselvan, "A Practical approach to the Design and Implementation of Speed Controller for Switched Reluctance Motor Drive using Fuzzy Logic Controller", Journal of Electrical Engineering, vol.58, No.1, 2007, pp. 39-46. [8] Vikas S. Wadnerkar, Dr.G.TulasiRam Das, Dr.A.D.Rajkumar, “Performance Analysis of Switched Reluctance Motor; Design, Modeling and Simulation of 8/6 Switched Reluctance Motor”, Journal of Theoretical and Applied Information Technology, 2005-2008. [9] KOPECKÝ, M.: Paper to Control of Switched Reluctance Motor. PhD thesis, VŠB-Technical University of Ostrava, 2002. [10] NEBORAK, I.: Modelling and Simulation of Electrical Control Drives. VŠB-Technical University of Ostrava, 2002. ISBN 80-248- 0083-7. [11] VAS, P.: Artificial-Intelligence-Based Electrical Machines and Drives. Oxford science publication, 1999. ISBN019859397X. [12] Mahavir Singh Naruka, D S Chauhan and S N Singh, “Power Factor Improvement in Switched Reluctance Motor Drive using PWM Converter”, International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 4, 2013, pp. 48 - 55, ISSN Print: 0976-6545, ISSN Online: 0976-6553. [13] Pradeep B Jyoti, J.Amarnath and D.Subbarayudu, “Application of Neuro-Fuzzy Controller in Torque Ripple Minimization of Vector Controlled VSI Induction Motor Drive”, International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 3, 2013, pp. 121 - 127, ISSN Print: 0976-6545, ISSN Online: 0976-6553.