Abstract-
This paper presents simulation results for threephase, 6/4 poles and four phase, 8/6 poles switchedreluctance motor (SRM). The main focus of this paper is toinvestigate the dynamic performance of switchedreluctance (SR) motors. This investigation is achievedthrough simulation using MATLAB/SIMULINK andFuzzy Logic Control (FLC).Index Terms-Switched Reluctance Machines, DynamicSimulation, Matlab , Simulink, Fuzzy Control.
I. INTRODUCTION
The switched reluctance machine’s (SRM) principleof operation has been known for more than a century,under general name of the doubly salient variablereluctance motor. However, an intensive research onSRM began about thirty years ago, mainly due to theprogress in power electronics and microprocessors. Itsprincipal advantages are simple and robust construction,possibility to work at very high speeds, high mechanicaltorque at low speeds, and simple power electronic drives[1-6].Many electrical machine researchers are investigatingthe dynamic behaviour of switched reluctance motor(SRM) by monitoring the dynamic response (torque andspeed), minimising the torque ripples, building differenttypes of controllers to reduce the cost, to increase thegeneral performance of SRM and reliability. In thepresent study, the switched reluctance motor issimulated to study the dynamic performance usingMatlab / Simulink environment. It is very useful andpowerful simulation tool and provides greater flexibilityto simulate various features of SR motors’s performance[7-10].Fuzzy logic is a powerful problem-solvingmethodology with a myriad of applications in embeddedcontrol and information processing. Fuzzy logicprovides a remarkably simple way to draw definiteconclusions from vague, ambiguous or impreciseinformation. Fuzzy logic resembles human decisionmaking with its ability to work from approximate dataand find precise solutions. Unlike classical logic thatrequires a deep understanding of a system, exactequations, and precise numeric values, fuzzy logicincorporates an alternative way of thinking, whichallows modelling complex systems using a higher levelof abstraction, originating from our knowledge andexperience. In 1965 Lotfi A. Zadeh published hisseminal work "Fuzzy Sets” which described themathematics of fuzzy set theory, and by extension fuzzylogic [11,12]. This theory proposed making themembership function (or the values false and true)operate over the range of real numbers [0.0, 1.0]. Newoperations for the calculus of logic were proposed, andshowed to be in principle at least a generalization ofclassic logic. Many researchers extended the work andapplied to control problems [13-15]. SR motor is highlynonlinear electromagnetic structure. Fuzzy logic controlis a powerful technique to study the simulation andcontrol aspects. Many researchers have employed fuzzylogic to study various aspects of SR motor drivesystems [16-22]. The authors have used fuzzy logic tosimulate 8/6 and 6/4 pole SR motors
.II. MATLAB SIMULATION
Matlab / Simulink package is used to simulate the SRmotor and look up table of the torque τ (θ, I) is used torepresent the simulation. The torque is a function ofrotor position and current, which is extracted from thenumerical data of the motor design by a finite elementsmethod [23]. The look up table from Simulink library isused to represent the developed torque in the 3 phase,6/4 poles base SRM and 3 phase, 6/4 poles optimizedSRM. The displacement angles are considered as a rowparameters (horizontal parameters), that varied between(0° to 45°), and the mmf is considered as columnparameters (vertical parameters), which is variedbetween (30 to 210 AT).This simulation is based on equation (1), where T isthe torque, Τeis the electromechanical torque, Τlis theload torque, and ω is the rotational velocity of the shaft,J moment of inertia. Figure 1 shows how to multiply thedeveloped torque table by moment of inertia (1/J), andthen through single and double integration, ω and θhave been obtained.


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