Simulation Of Speed Control Of Brushless Dc Motor, With Fuzzy Logic .
International Journal of Electrical, Electronics and Data Communication, ISSN: 2320-2084Volume-2, Issue-4, April-2014SIMULATION OF SPEED CONTROL OF BRUSHLESS DC MOTOR,WITH FUZZY LOGIC CONTROLLER1C. SHEEBA JOICE, 2P.NIVEDHITHA1,2Department of Electronics and Communication Engineering, Saveetha Engineering College, Chennai, IndiaEmail: sheebajoice@saveetha.ac.in, nivedhitha.sembian@gmail.comAbstract— The electronically commuted Brushless DC motors are widely used in many industrial applications whichincrease the need for design of efficient control strategy for these noiseless motors. This paper deals with the efficient speedcontrol mechanisms for these drives using meaningful fuzzy sets and rules. The fuzzy logic controller is developed using aMATLAB/ Simulink tool. The paper deals with the possibility of designing a control strategy, to achieve accurate speedcontrol with the advantages of low cost. The proposed method is simple and efficient compared with the conventionalcontrollers.Keywords— BLDC motor drive, Fuzzy Logic Controllers, Fuzzy sets and Fuzzy rules, Speed control.I.The rotor position is sensed which enablescommutation logic for the three phase inverter circuitsthat contain MOSFET switches.INTRODUCTIONA. BLDC Motor with Hall SensorsBrushless DC motors works similar to theconventional DC motor with the mechanicalcommutation replaced by an electronically controlledcommutation system. These motors have the rotatingpermanent magnets and stationary armature. TheBLDC motor that are utilized in the proposed controldesign is star connected BLDC motor. The powerdistribution is achieved by the intelligent electroniccontroller. The electronic controller requires rotorposition information for proper commutation ofcurrents in the respective stator windings. The rotorposition can be sensed using Hall effect sensorsembedded in the stator and thus stator windings areenergized accordingly.BLDC motor drive control can be done in sensor orsensor less mode. Though the sensor less control offersthe advantage of reduced cost, the sensor less controloffers low performance at transients or low speedrange with increase in complexity of the controlelectronics and algorithms makes the use of Hallsensors more efficient. Embedded control of BLDCmotors using dsPIC30f4013 generates a PWM signalthat controls the inverter topology there by controllingthe drive. High flexibility of control can be obtained byimplementing efficient control algorithm in thecontroller .TABLE IClockwise Hall Sensor Signals and Drive re Ha, Hb, Hc represents the Hall sensor signals.Q1 - Q6 represents the MOSFETs in the Switchingcircuit.The Hall sensors should be kept 120 apart to obtainsymmetrical operation of motor phases. With the rotorposition sensed, three bit codes of Hall sensed signal isobtained as shown in TABLE I. Each code valuespecifies the rotor position and the correspondingstator windings that are to be energized. Ha, Hb, Hcsignals are high or low depending whether the sensoris near the N or S pole of the rotor magnets.Depending on these signals the switches Q1 - Q6 areON/OFF. From TABLE I it is seen that when HC ishigh, the switch Q4-Q5 conducts energizing thecorresponding stator windings are energized. DigitalPWM signals are generated and Speed regulation isachieved by using high and low level duty cycles.B. Fuzzy Logic ControllerThe speed control of BLDC drive can be simulatedusing the fuzzy logic controller. The Fuzzy logicsystem plays a central role in the controlling of linearFig.1. Bldc Motor Transverse Section with Hall SensorsSimulation Of Speed Control Of Brushless Dc Motor, With Fuzzy Logic Controller24
International Journal of Electrical, Electronics and Data Communication, ISSN: 2320-2084systems and in industrial applications where thecontrol and automation plays a vital role. The fuzzylogic control is designed using the fuzzy inferencesystems with the definition of input and outputmembership functions. The fuzzy sets and rules aredesigned and accordingly the drive can be controlled.With the usage of single antecedent fuzzy rule theintersection of fuzzy rule problem can be eliminated.With the fuzzy rules designed the desired control canbe achieved.The complete drive system can be modeled withMATLAB/Simulink tool by categorizing the modelinto BLDC motor, switching circuit/Inverter topology,PWM driver circuit and the Controller circuit. TheFuzzy combined controllers can also be used if thereexists a need to combine all local fuzzy controllers thatminimizes the chattering effects and the stability isimproved. Fuzzy rule bases are determined by theFuzzy clustering methods (FCM) to obtain themembership functions that are utilized in the design offuzzy rules for the generation of PWM pulses.This paper describes the speed control of the BLDCmotor drive designed with fuzzy logic controller thatis simulated and the dynamic characteristics areobtained and analyzed using the MATLAB/SimulinkTool. This paper is organized as follows. Section IIdescribes the mathematical model. Section IIIdescribes the Fuzzy sets and rules evaluation for Speedcontrol of BLDC motor. Section IV describes theMATLAB/Simulink model. Section V provides theresults of the Simulink model and its outputs areanalyzed. Section VI concludes the system with thefuture prospects of the design.II.a Volume-2, Issue-4, April-2014Rb Rc R(4)aa Lbb Lcc Ls(5)ba Lab Lca Lac Lbc Lcb M(6) (7)Since a ib ic 0, and with (Ls – M) L, we have (8)R: Stator Resistance per phase assumed to be equal forall phases.Ls: Stator inductance per phase assumed to be equalfor all phases.M: Mutual inductance between the phases.ia,, ib, ic - Stator current /phase.The instantaneous induced EMFs can be written as inequation (9)-(11)a far)λpωm(9)b fbr)λpωm(10) f)λωc cr p m(11)Where ωm, is the rotor mechanical speed and r is therotor electrical position.With the rotor position being sensed the three phaseswitching sequence can be illustrated using Fig.2.MODEL DESCRIPTIONA. Mathematical Model of BLDC Motor DriveIn a brushless motor, the rotor incorporates themagnets, and the stator contains the windings. As thename suggests brushes are absent and hence in thiscase, commutation is implemented electronically witha drive amplifier that uses semiconductor switches tochange current in the windings based on rotor positionfeedback. In this respect, the BLDC motor isequivalent to a reversed DC commutator motor, inwhich the magnet rotates while the conductors remainstationary. Therefore, BLDC motors often incorporateeither internal or external position sensors to sense theactual rotor .The principle of operation and the dynamic model ofBLDC motor can be explained as follows. The circuitequations of the stator windings in terms of electricalconstants is given by equations (1)-(8)an Raia aa ia Lbaib Lca ic) ea(1)bn Rbib ab ia Lbb ib Lcb ic) eb(2)cn Rcic ac ia Lbc ib Lcc ic) ec(3)Fig.2.Three Phase Switching SequenceThe switching instant of the individual transistorswitches, Q1-Q6 with respect to the trapezoidal EMFwave is shown in the Fig.2.It is seen that the EMFwave is synchronized with the rotor. So switching thestator phases synchronously with the EMF wave makethe stator and rotor mmfs rotate in synchronism. Thus,the inverter acts like an electronic commutator thatreceives switching logical pulses from the rotorposition sensor. This is why a BLDC drive is alsocommonly known as an electronically commutatedmotor (ECM).B. Fuzzy Logic ControllerIn recent years, fuzzy control has emerged as apractical alternative to classical control schemes whenone is interested in controlling certain time varying,non-linear, and ill-defined processes. There have infact been several successful commercial and industrialSimulation Of Speed Control Of Brushless Dc Motor, With Fuzzy Logic Controller25
International Journal of Electrical, Electronics and Data Communication, ISSN: 2320-2084applications of fuzzy control. Fuzzy controllers areused to control consumer products, such as washingmachines, video cameras, and rice cookers, as well asindustrial processes, such as cement kilns,underground trains, and robots. Fuzzy control is acontrol method based on fuzzy logic. Fuzzy logic canbe described simply as computing with words ratherthan numbers; fuzzy control can be described simplyas control with sentences rather than equations. Afuzzy controller can include empirical rules, and thatis especially useful in operator controlled plants.Fuzzy logic controller (FLC) is capable of improvingits performance in the control of a nonlinear systemwhose dynamics are unknown or uncertain. Fuzzycontroller is able to improve its performance withouthaving to identify a model of the plant. Fuzzy controlis similar to the classic closed-loop control approachesbut differs in that it substitutes imprecise, symbolicnotions for precise numeric measures.Fuzzy controllers are more robust because they cancover a wide range of operating conditions. Fuzzycontrollers are more flexible and the modifications ofthe Fuzzy rules are simpler when compared to theconventional controllers. With these benefits Fuzzycontrollers can be utilized as industrial tool for controlapplications.The fuzzy controller takes input values from the realworld. These crisp input values are mapped to thelinguistic values through the membership functions inthe fuzzification step. A set of rules that emulates thedecision making process of the human expertcontrolling the system is then applied using certaininference mechanisms to determine the output.Finally, the output is mapped into crisp control actionsrequired in practical applications in thede-fuzzification step.In a fuzzy controller the data passes through apre-processing block, a controller, and apost-processing block. Pre-processing consists of alinear or non-linear scaling. Linguistic variables arecentral to fuzzy logic manipulations. They arenon-precise variables that often convey a surprisingamount of information. Usually, linguistic variableshold values that are uniformly distributed (µ) between0 and 1, depending on the relevance of a contextdependent linguistic term.The collection of rules is called a rules base and therules are in the familiar if-then format, and formallythe if-side is called the condition and the then-side iscalled the conclusion. The computer is able to executethe rules and compute a control signal depending onthe measured inputs error and change in error.Therefore the rules reflect the strategy that the controlsignal should be a combination of the reference errorand the change in error. Fuzzy inference is the processof formulating the mapping from a given input to anoutput using fuzzy logic.The mapping then provides a basis from whichVolume-2, Issue-4, April-2014decisions can be made. The process of fuzzy inferenceinvolves membership functions, fuzzy logic operators,and if-then rules. There are two types of fuzzyinference systems that can be implemented in thefuzzy logic toolbox which are Mamdani-type andTakagi–Sugeno (T–S) type. The basic structure of aMamdani- type F.L.C as illustrated in fig.3 belowconsists of the following components:Fuzzification, which converts controller inputs intoinformation that the inference mechanism can easilyuse to activate and apply rules.Rule-Base, (a set of If-Then rules), which contains afuzzy logic quantification of the expert’s linguisticdescription of how to achieve good control.Inference Mechanism, (also called an “inferenceengine” or “fuzzy inference” module), which emulatesthe expert’s decision making in interpreting andapplying knowledge about how best to control thesystem.Defuzzification Interface, which converts theconclusions of the inference mechanism into actualinputs for the process.Fig.3. Basic block diagram of flcIII. Fuzzy sets and rules evaluation- Speed control ofBLDC Motor driveThe basic block diagram of the speed control of BLDCmotor drive using Fuzzy logic controller is illustratedin Fig.4.The error signal generated as the result ofvariation in the reference speed and the actual speed ofthe motor sensed by the hall signals is utilized for theformulation of Fuzzy rules which results in thegeneration of the PWM signals to drive the switchingcircuit and with flexibility of fuzzy controllers widerange of speed can be controlled using this Fuzzycontroller.Fig.4.Block Diagram of Fuzzy Controlled Bldc Motor DriveSimulation Of Speed Control Of Brushless Dc Motor, With Fuzzy Logic Controller26
International Journal of Electrical, Electronics and Data Communication, ISSN: 2320-2084Volume-2, Issue-4, April-2014To evaluate the disjunction of the rule antecedents theOR fuzzy operation is used. Fuzzy expert systemsmake use of the classical fuzzy operation unionexpressed in equation(12),(12)AB (x) max( AB (x) )Similarly, in order to evaluate the conjunction of therule antecedents, the AND fuzzy operation is used andthe classical fuzzy operation intersection is given byequation (13).(13)AB (x) min( AB (x) )The min-max compositional rule of inference is used.There are several defuzzification methods, in thisdesign the centroid technique specified in Fig.7 isutilized. It finds the point where a vertical line wouldslice the aggregate set into two equal masses.A. Steps in Fuzzy Decision algorithm:Step1: The Fuzzy rules are designed and the rules thatare verified are invoked using the membershipfunctions and the truth values obtained.Step2: The result is mapped to the membershipfunction and the variable to control the outputvariable.Step3: The final step is the defuzzification providingthe crisp output needed to control the system. Thecombination of fuzzy operation and rule basedinference system provides a fuzzy expert system.Fig.5.Flow Model Of Fuzzy Speed Reference ControlThe fig.5 represents the flow model for fuzzy speedreference control.This Fuzzy flow model describes the conversion of allcrisp inputs of both the reference model and the modelto be controlled into the fuzzy inputs.The purpose of the Model Reference Adaptive FuzzyControl (MRAFC) specified in Fig.5 is to change therules definition in the direct fuzzy logic controller(FLC) and rule base table according to the comparisonbetween the reference model output signal and systemoutput. With MRAFC, good tracking characteristicswere obtained even under severe variations of systemparameters. The MRAFC observes the model outputsand adjusts the rules in a direct fuzzy controller, sothat the overall system control capability is improved.High performances and robustness have been achievedby using the MRAFC.Fig.7.Centroid Defuzzification MethodMathematically this centre of gravity (COG) can beexpressed as: (14)Where denotes the algebraic sum, representscentroid of each member ship function. Thus thefuzzification, inference and defuzzification areperformed using equation (14).Fig.8 represents the fuzzy inference system of thedesigned fuzzy controller. Fuzzy inference systemcontains the input signals and output signals thatprovide the input membership functions and theoutput membership functions.B. Fuzzy Membership Functions:The membership functions illustrated in Fig.6 used tofuzzification two input values and defuzzificationoutput of the fuzzy controller. For seven clusters in themembership functions, seven linguistic variables aredefined as: Negative Big (NB), Negative Medium(NM), Negative Small (NS), Zero (Z), Positive Small(PS), Positive Medium (PM), and Positive Big (PB).Fig.8.Fis For Speed Control Of Bldc MotorThe hall signals senses the rotor position, with therotor position corresponding speed is detected. Thedesired speed of the motor is known. The inferenceengine specified (motor) in Fig.8contains the FuzzyFig.6 Membership Function Of FlcSimulation Of Speed Control Of Brushless Dc Motor, With Fuzzy Logic Controller27
International Journal of Electrical, Electronics and Data Communication, ISSN: 2320-2084rules that produces the corresponding PWM signals.The input member ship function of the two inputs ofthe system is represented in Fig. 9 and Fig. 10. TheFuzzy system contains two input membershipfunctions, one is the hall signal and the other is thespeed signal. It contains PWM signals as one outputmembership function.Volume-2, Issue-4, April-2014The developed MATLAB model in Fig.12 providesthe speed control of BLDC Motor using Fuzzy logiccontroller. The simulation results provide thenecessary waveforms for the analysis of speed controlof BLDC motor drives.Fig.12.matlab/simulink model of bldc motor using fuzzy logiccontroller.Fig.9 input membership function of hall signalThe implemented Fuzzy rules provide the followingwaveforms in the speed control of BLDC Motor drive.IV.RESULTS AND DISCUSSIONThe Hall sensor signals that is the signals with respectto the rotor position of the BLDC motor are generated.In reference to these Hall signals the PWM signals aregenerated. The PWM signals generated provide thecontrol signals for the switching circuits that energizethe stator windings accordingly and the actual speedof the motor is varied with respect to the referencespeed.The reference speed of the BLDC motor is seen in theoscilloscope as in Fig.15.Fig. 10 input membership function of actual speed signalThe corresponding output membership functions ofthe PWM signals are represented in the Fig.11Ref 060.080.10.12Time(Sec)0.140.160.180.2Fig.15. Reference speed of bldc motor driveThe speed of the motor with Fuzzy logic controller isseen as in Fig.16.Fig.11 Output Memebership Function Of Pwm SignalsAc tual Speed3000The Fuzzy Inference system is designed with theFuzzy rules specified in the Mamdani type ofFIS.With the designed fuzzy rules the PWM signalsare generated that provides the necessary gate signalsfor the switchng of the Inverter bridge circuit thatenergises the respective windings of the three phaseBLDC motor and hence the speed of the motor iscontrolled as desired.III.2500Speed(rpm)2000150010005000SIMULINK Fig.16.Speed Of Bldc Motor Using Fuzzy ControllerSimulation Of Speed Control Of Brushless Dc Motor, With Fuzzy Logic Controller280.2
International Journal of Electrical, Electronics and Data Communication, ISSN: 2320-2084The results obtained shows that the actual speed isapproximately equal to the reference speed. Thus anefficient speed control is achieved, for a BLDC Motorusing Fuzzy Logic Controller. The comparison curvesof the actual speed and reference speed obtained usingthe simulation inspector tool is illustrated as in Fig.17Comparison speed .040.060.080.10.12Time(sec)0.140.160.18Volume-2, Issue-4, April-2014[2] B. Mahesh Kumar, G. Ravi, and R. Chakrabarti “SensorlessSpeed Control of Brushless DC Motor with Fuzzy BasedEstimation,” Iranian Journal Of Electrical and ComputerEngineering, Vol. 8, N0. 2, pp.119-125, Summer-Fall, 2011.[3] Radu Duma, Petru Dobra, Mirela Dobra and Ioan Valentin Sita“Low Cost Embedded Solution for BLDC Motor Control,”International conference on System Theory, Control andComputing,pp.1-6,Aug 2011.[4] Anand Sathyan, Nikola Milivojevic, Young-Joo Lee, MaheshKrishnamurthy and Ali Emadi “An FPGA-Based Novel DigitalPWM Control Scheme for BLDC Motor Drives,” IEEETransactions On Industrial Electronics, Vol. 56, No.8,pp.3040-3049 Aug 2009.[5] Pooya Alaeinovin, Juri Jatskevich, “Filtering of Hall-SensorSignals for Improved Operation of Brushless DC Motors”IEEE Transactions On Energy Conservation, Vol. 27, No. 2,pp.547-549,Jun 2012.[6] Chwan-Lu Tseng, Shun-Yuan Wang, Shao-Chuan Chien, andChaur-Yang Chang “Development of a Self-TuningTSK-Fuzzy Speed Control Strategy for Switched ReluctanceMotor,” IEEE Transactions on Power Electronics, vol. 27, No.4,pp 2141- 2151,April 2012.[7] Shun-Chung Wang and Yi-Hwa Liu “A Modified PI-LikeFuzzy Logic Controller for Switched Reluctance Motor Drives,IEEE Transactions on Industrial Electronics, Vol. 58, No. 5,pp.1812- 1825, May 2011.[8] V. U, S. Pola, and K. P. Vittal, “Simulation of four quadrantoperation & speed control of BLDC motor onMATLAB/SIMULINK,” in Proc.IEEE Region 10Conference,pp.1-6, Nov 2008.[9] Amit Vilas Sant and K. R. Rajagopal “PM Synchronous MotorSpeed Control Using Hybrid Fuzzy-PI With Novel SwitchingFunctions” IEEE Transactions On Magnetics, Vol.45,N0,10,pp 4672- 4675 October 2009.[10] Vicente Milanés, Jorge Villagrá, Jorge Godoy, and CarlosGonzález, “Comparing Fuzzy and Intelligent PI Controllers inStop-and-Go Manoeuvres” IEEE Transactions On ControlSystems Technology, Vol. 20, No. 3, pp.770-778, May 2012.[11] Yee-Pien Yang and Yi-Yuan TingKumar “Improved AngularDisplacement Estimation Based on Hall-Effect Sensors forDriving aBrushless Permanent-Magnet Motor,” IEEETransactions On Industrial Electronics, Vol. 61, No. 1,pp.504-511 Jan2014.[12] M. Surya Kalavathi, and C. Subba Rami Reddy “PerformanceEvaluation of Classical and Fuzzy Logic Control Techniquesfor Brushless DC Motor Drive” IEEE Transactions OnIndustrial Electronics, Vol. 61, No. 1 , pp .488-491, Jul 2012.[13] Xiang Wang, Mei Li “Rotor Position Simulation of SwitchedReluctance Motor Based on Fuzzy Inference Rules,”International Conference on Innovation Management,pp.75-78, Sep 2009.[14] Chang-Han Jou, Jian-Shiun Lu, and Mei-Yung Chen “AdaptiveFuzzy Controller for a Precision Positioner UsingElectro-Magnetic Actuator,” International Journal of FuzzySystems, Vol. 14, No. 1,pp.110-117, March 2012.[15] Han Ho Choi and Jin-Woo Jung, “Discrete-Time Fuzzy SpeedRegulator Design for PM Synchronous Motor” IEEETransactions OnIndustrial Electronics, Vol. 60, No.2,pp.600-607, Feb 2013.[16] N.T.-T. Vu, H.H. Choi, R.-Y. Kim, J.-W. Jung “Robust speedcontrol method for permanent Magnet synchronous motor,”IET Electric Power Applications, vol.6, No.7, pp.399 -411, Feb2012.[17] Timothy J. Ross, Fuzzy Logic with Engineering Applications,2nd ed, England: John Wiley & Sons Ltd, 2004.0.2REF.SPEED, ACTUAL SPEEDFig.17.comparison of refernce and actual speed curvesWith the graph obtained it is observed that theefficiency of the designed Fuzzy Logic Controller iscalculated as 98.1% which proves to be efficient thanthe conventional controllers.CONCLUSIONIn this paper the control scheme for the speed controlof BLDC motor using Fuzzy logic controller isproposed. The significant advantages of the proposedwork are: (1) simplicity of control i.e. the fuzzy rulebase or Fuzzy set can be easily modified (2) Increasedrobustness. The simulation of Fuzzy Logic controller,using MATLAB to control the speed of flexible BLDCMotor, proves that the desired speed is attained with ashorter response time, when compared withconventional controllers. The dynamic characteristicsof the motor is obtained and the analysis reveals thatFuzzy controller is a highly controller and is capableof controlling the motor drive over wide speed range.The fuzzy controller proves to be more efficient thanthe conventional controller. The simulated Fuzzycontrol will be implemented, using dsPIC30F4013. Aprototype model will be developed to analyzecharacteristics and the hardware results will becompared with the results of conventional controllers.REFERENCES[1] C. Sheeba Joice, S. R. Paranjothi, and V. Jawahar SenthilKumar “Digital Control Strategy for Four Quadrant Operationof Three Phase BLDC Motor With Load Variations, ” IEEETransactions On Industrial Informatics, Vol. 9, No. 2, pp.974 –982, May 2013. Simulation Of Speed Control Of Brushless Dc Motor, With Fuzzy Logic Controller29
The fuzzy logic control is designed using the fuzzy inference systems with the definition of input and output membership functions. The fuzzy sets and rules are designed and accordingly the drive can be controlled. With the usage of single antecedent fuzzy rule the intersection of fuzzy rule problem can be eliminated.
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