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Dc24672680

  1. 1. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, June-July 2012, pp.672-680 A Comparative Analysis Of PI And Neuro Fuzzy Controllers In Direct Torque Control Of Induction Motor Drives A.Sudhakar*,M.Vijaya Kumar** *(Department of Electrical and Electronics Engineering,S.V.I.T Engg College, Anantapur,India) **(Department of Electrical Engineering,J.N.T.U,Anantapur,India.)Abstract: The implementation of conventional harmonic currents.DTC in Induction Motor drives consisting of Various methods have been proposed toPI torque controller suffers from complex overcome these drawbacks, such as variabletuning and Overshoot problems. One of the structure control approach,fuzzy logicvarious methods to tackle this problem is control,neural netwok control,adaptive controlimplementation of intelligent controllers like methods have been proposed for motion controlneuro fuzzy-based controller This paper of Induction motor drive[2-12]. The PI controlpresents the simulation and analysis of a is simple and offers a wide stability margin, butNeuro fuzzy-based torque controller for DTC it incorporates tuning,overshoot problems. Theof Induction motor drives. This control fuzzy logic can compensate the systemscheme uses the speed error calculated from nonlinearities through human expertise. Yet, itreference speed and estimated speed which relies too much on the intuition and experiencegenerates the estimated Torque and of the designer. The neural network can handlecompared with the actual Torque and the complicated nonlinear characteristics of thegenerates the inverter switching states. In this system, but suffer from the problem of lengthypaper a modified ANFIS structure is training and convergence time. The adaptiveproposed. This structure generates the desired control can self adjust the controller parametersreference voltage which regulates the to adapt system parameter variations.performance of induction motor. Unfortunately, it generally requires a referenceComparisons and analysis under various model of the system.operating conditions between hysteresis-basedPI torque controller and Neuro fuzzy-based The controller proposed in this paper istorque controller are presented. The results neuro fuzzy-based controller which gives bettershow that the proposed controller managed to performance compared to PI controller. Thereduce the overshoot and give better neural network is well known for its learningperformance. ability and approximation to any arbitrary continuous function.[9] It has been proposed inKeywords-direct torque control, induction the literature that neural networks can be appliedmotor drive, PI controller, complex tuning, to parameter identification and state estimation .Overshoot, neuro fuzzy controller. The fuzzy logic controller solves the problem of non-linearities and parameter variations of IM1 Introduction drive. It achieves high dynamic performance and Direct torque control(DTC) of induction accurate speed control with good steady-statemotor drives has gained popularity due to its characteristics. The fuzzy rules and membershipsimple control structure and sensorless operation. functions are tuned to give better performance.The structure of conventional DTC drive The proposed neuro fuzzy-based controller hasoriginally introduced in [1], is simple. It consists been simulated using MATLAB/SIMULINK.of torque and flux hysteresis-based controllers, The performance of the proposed controller isflux and torque estimator switching look-up evaluated under various operating conditions.table. Despite of its simplicity, it was well The simulation results confirm the efficacy of theknown that the implementation of the hysteresis- control system.based DTC –PI controller requires fine tuning This paper is organized as follows : firstand cannot cope with parameters variation.. This the machine modeling of Induction Motor,resulted in unpredictable switching frequency of second the direct torque control strategy using PIthe switching devices. Further the variable controller, third the direct torque control strategyswitching frequency will generate unpredictable using neuro fuzzy-based controller, fourth 672 | P a g e
  2. 2. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, June-July 2012, pp.672-680Simulation results at different operating λqr = Lriqr + Lmiqs (2)conditions, fifth conclusions. The electromagnetic torque in the stationary2.Induction Motor Modelling reference frame is given as The induction motor model can bedeveloped from its fundamental electrical and Te = (3/2)(P/2)(λdsi qs −λqsids) (3)mechanical equations. In the stationary referenceframe the voltage equations are given by 3. DTC with PI ControllerVds = Rsids + pλds In the DTC scheme [1] (Figure 1), theVqs = Rsiqs + pλqs electromagnetic torque and flux signals are0 = Rr idr + ωr λqr + pλdr delivered to two hysteresis comparators. The0 = Rr iqr - ωr λdr + pλqr (1) corresponding output variables and the stator flux position sector are used to select the Where p indicates the differential appropriate voltage vector from a switching tableoperator(d/dt). The stator and rotor flux linkages which generates pulses to control the powerare defined using their respective self leakage switches in the inverter. This scheme presentsinductances and mutual inductances as given many disadvantages (variable switchingbelow frequency - current and torque distortion caused by sector changes - start and low-speed operationλds = Lsids + Lmidr problems ).λqs = Lsiqs + Lmiqrλdr = Lridr + Lmids Figure 1 : Direct Torque Control scheme with PI Controller All the schemes cited above use a PIcontroller for speed control. The use of PI Fuzzy logic and artificial neuralcontrollers to command a high performance networks can be combined to design a directdirect torque controlled induction motor drive is torque neuro fuzzy controller. Human expertoften characterised by an overshoot during start knowledge can be used to build an initialup. This is mainly caused by the fact that the artificial neural network structure whosehigh value of the PI gains needed for rapid load parameters could be obtained using online ordisturbance rejection generates a positive high offline learning processes. To eliminate thetorque error. This will let the DTC scheme take above difficulties, a Direct Torque Neuro Fuzzycontrol of the motor speed driving it to a value Control scheme (DTNFC) has been proposed.corresponding to the reference stator flux. Atstart up, the PI controller acts only on the error The Neuro Fuzzy inference systemtorque value by driving it to the zero border. (ANFIS) is one of the proposed methods toWhen this border is crossed, the PI controller combine Fuzzy logic and artificial neuraltakes control of the motor speed and drives it to networks[17,21,22]. Figure 2 shows the adaptivethe reference value. NF inference system structure It is composed of five functional blocks (rule base, database, a4. DTC with Neuro-fuzzy controller decision making unit, a fuzzyfication interface 673 | P a g e
  3. 3. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, June-July 2012, pp.672-680and a defuzzyfication interface) which are The block scheme of the proposed self-generated using four network layers: tuned direct torque neuro-fuzzy controllerLayer 1: This layer is composed of a number of (DTNFC) for a voltage source PWM inverter fedcomputing nodes whose activation functions are induction motor is presented in Figure 3. Thefuzzy logic membership functions (usually, internal structure of the NFC is shown in Figuretriangular or bell-shaped functions). 2.Layer 2: This layer chooses the minimum valueof the inputs. In the first layer of the NF structure,Layer 3: This layer normalises each input with sampled speed error we ,multiplied by respectiverespect to the others (The ith node output is the weights wψ and wT , is mapped through threeith input divided the sum of all the other inputs). fuzzy logic membership functions. TheseLayer 4: This layer’s ith node output is a linear functions are chosen to be triangular shaped asfunction of the third layer’s ith node output and shown in Figure 2.The second layer calculatesthe ANFIS input signals and sums all the input the minimum of the input signals. The outputsignals. The ANFIS structure can be tuned values are normalised in the third layer, to satisfyautomatically by a least-square estimation (for the following relation:output membership functions) and a back σi = wi / (Σk wk) (4)propagation algorithm (for output and inputmembership functions). where wi and σi are the ith output signal of the second and third layer respectively. σi is considered to be the weights. Figure 2 : Proposed Neuro fuzzy structure Controller Figure 3 : Triangular membership function sets 674 | P a g e
  4. 4. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, June-July 2012, pp.672-680 Figure 4 : Direct Torque Control scheme with Neuro Fuzzy Controller The Neuro Fuzzy speed controller inverter which in turn controls the Torquecombines fuzzy logic and artificial neural parameter of Induction motor[12,15,26].networks to evaluate the reference torque. This .evaluation is performed using the reference 5. Simulation Resultsspeed and actual speed errors. This calculated Direct Torque control scheme with PIerror gives the estimated torque value which is controller and Neuro fuzzy controller arecompared with the actual torque value in the implemented in the Induction motor drive withHysteresis comparator. The corresponding the following parameters under various operatingtorque,flux from hysteresis comparators and conditions at no load and sudden change in loadangle estimated from Flux Torque estimator are applied to the motor. All the results aregiven as inputs to Switching table which represented taking time as the x-axis.generates the appropriate voltage vector for the V=260/50Hz. Rs=0.9Ω Rr=1.227Ω P=2 Ls=150.64mH, Lr=150.64mH. Lm=143.84mH Figure 5 : Torque Characteristics at no load with PI Controller 675 | P a g e
  5. 5. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, June-July 2012, pp.672-680 Figure 6 : Torque Characteristics with sudden change in load Torque from -6.4Nm to 6.4Nm with PI Controller Figure 7 : Speed Characteristics at no load with PI ControllerFigure 8: Speed Characteristics at sudden change in load Torque from -6.4Nm to 6.4Nm with PI Controller Figure 9 : Stator flux response with PI controller 676 | P a g e
  6. 6. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, June-July 2012, pp.672-680 Figure 10 : Magnetizing and Torque components of stator current with PI controller Figure 11: Torque Characteristics at no load with NeuroFuzzy ControllerFigure 12 : Torque Characteristics at sudden change in load Torque from -6.4Nm to 6.4Nm with NeuroFuzzy Controller Figure 13 : Speed Characteristics at no load with NeuroFuzzy Controller 677 | P a g e
  7. 7. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, June-July 2012, pp.672-680Figure 14 : Speed Characteristics at sudden change in load Torque from -6.4Nm to 6.4Nm with NeuroFuzzy Controller Figure 15 :Stator flux response with Neuro fuzzy controller Figure 16 : Magnetizing and Torque components of stator current with Neuro fuzzy controller 678 | P a g e
  8. 8. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, June-July 2012, pp.672-680 Parameters PI Neuro-fuzzy Controller Controller Overshoot 47% 7% Computational effort 21.6µs 97.2µs Table I : Comparison between PI Controller and Neuro fuzzy Controller The overshoot and time for Transcations on Idustrial Electronics, volcomputational effort for PI controller and Neuro 47, no 2,2000, pp 380-388.fuzzy controller in Direct Torque control of [5] L. Mokrani and R. Abdessemed., (2003)Induction Motor are estimated. Table I shows A fuzzy self-tuning P1 controller forthat DTNFC scheme gives better performances speed control of induction motor drive,than the conventional DTC scheme with PI Proceedings of IEEEConference oncontroller. We can remark however that the high Control Applications, CCA , vol. 1, 23-25value of the DTNFC scheme regarding ,2003,pp 785-790.computational effort does not affect the control [6] W J Wang and J Y Chen., Compositivecycle since it stays below 50% of its value. adaptive position control of induction motors based on passivity theory, IEEE Transactions on Energy Conversion,6. Conclusions vol.16, no.2,,2001,pp. 180- 185. In this paper, both Direct torque [7] T C. Chen and T T. Sheu., Modelcontrolled PI controller and neuro fuzzy reference neural network controller forControllers are discussed.The PI controller induction motor speed control" , IEEEcannot prevent DTC scheme from driving the Transactions on Energy Conversion, vol.motor speed to the stator flux corresponding 17, no.2,2002, pp.157- 163.speed. This will most likely result in a speed [8] M. N. Uddin, T S. Radwan and M. A.overshoot. Simulation of the DTNFC Induction Rahman., Performance of fuzzy logic-motor drive for speed control shows promising based indirect vector control for inductionresults. The motor reaches the reference speed motor drive, IEEE Trans. Indrapidly and with minimum overshoot. The Applications, vol. 38, no. 5,simulation results obtained show the ,2002,pp.1219-1225.effectiveness of neuro fuzzy controller in speed [9] B. Kosko. Neural Networks and Fuzzyregulation of induction motor. Systems: A Dynamic Systems Approach to Machine Intelligence. EnglewoodReferences Cliffs, NJ: Prentice-Hall,1992.[1] 1. Takahashi and T. Noguchi., A new [10] A. Miloudi, E. A. Alradadi, A. Draou A quick-response and high-efficiency new control strategy of direct torque fuzzy control strategy of an induction motor control of a PWM inverterfed induction IEEE Trans. IndAppl. Vol. IA-22, No, motor drive ”, Conf. Rec. ISIE2006, 5,1986 pp. 820-827. Montreal, CANADA, 09 – 13 ,2006.[2] E. C. Shin, T S. Park., W. H. Oh and J. Y [11] G. Buja A new control strategy of the Yoo A design method of PI controller for induction motor drives: The direct flux an induction motor with parameter and torque control,” IEEE Ind.Electron. variation. The 29th Annual Conference of Soc. Newslett., vol. 45,1998, pp. 14–16. the IEEE Industrial Electronics Society, [12] P. Vas Sensorless Vector and Direct IECON 03.vol 1, 2-6 2003, pp. 408 - 413. Torque Control. Oxford, U.K.: Oxford[3] C. F. Hu, R. B. Hong, and C. H. Liu., Univ. Press.,1998 Stability analysis and P1 controller tuning [13] D. Casadei, G. Serra, A. Tani, for a speed-sensorless vector-controlled Implementation of a Direct Torque induction motor drive 30th Annual Control Algorithm for Induction Motors Conference of IEEE Industrial Electronics based on Discrete Space Vector Society, IECON 2004, vol. 1, 2-6, 2004, Modulation IEEE Trans. Power Electron., pp.877 - 882. Vol. 15, N◦ 4,2000, pp. 769-777.[4] B. Robyns, F. Berthereau, J-P. Hautier, [14] P. Z. Grabowski, M. P. Kazmierkowski, and H. Buyse.,A fuzzy-logic based B. K. Bose, F. Blaabjerg, A Simple Direct multimodel field orientation in an indirect Torque Neuro Fuzzy Control of PWM FOC of an induction motor , IEEE Inverter Fed Induction Motor Drive IEEE 679 | P a g e
  9. 9. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, June-July 2012, pp.672-680 Trans. Ind. Electron., Vol. 47, No. 4, [21] J.-S. R. Jang, Self-learning fuzzy controllers 2000,pp. 863-870. based on temporal back propaga- , Mar./Apr. 1997. tion,IEEE Trans. Neural Networks, vol.[20] A. Damiano, P. Vas et a[15] M. P. 3,1992, pp. 714–723. Kazmierko wski, H. Tunia., Automatic [22] J.-S. R. Jang, ANFIS: Adaptive-network- Control of Converter-Fed Drives. based fuzzy inference system IEEE Trans. Amsterdam, The Netherlands: Syst., Man, Cybern., vol. 23, 1993, pp. Elsevier.,1994 665–684.[16] P. Tiitinen, P. Pohkalainen, J. Lalu, The [23] D. Casadei, G. Grandi, G. Serra, Study next generation motor control method: and implementation of a simplified and Direct torque control (DTC),EPE J., vol. efficient digital vector controller for 5, 1995, pp. 14–18. induction motors, in Proc. EMD’9 3,[17] J.-S. R. Jang, C.-T. Sun, Neuro-fuzzy Oxford, U.K.,1993, pp. 196–201. modeling and control Proc. IEEE, vol. 83, [24] M. Depenbrok, Direct self-control (DSC) 1995, pp. 378–406. of inverter fed induction machine IEEE[18] M. P. Kazmierkowski, A. Kasprowicz., Trans. Power Electron., vol. PE-3,1988, Improved direct torque and flux vector pp. 420–429. control of PWM inverter-fed induction [25] I. Boldea, S. A. Nasar, Torque vector motor drives IEEE Trans. Ind. Electron, control (TVC)—A class of fast and robust vol. 45,1995, pp. 344–350 . torque speed and position digital[19] J. N. Nash, Direct torque control, induction controller for electric drives in Proc. motor vector control without an encoder EMPS, vol. 15,1988, pp. 135–148. IEEE Trans. Ind. [26] T. G. Hableter, F. Profumo, M. Pastorelli, Applicat., vol. 33, 1997, pp. 333–341l., L. M. Tolbert, Direct torque control of Comparison of speed-sensorless DTC induction machines using space vector induction motor drives in Proc. PCI M, modulation IEEE Trans. Ind. Applicat., Nuremberg, Germany, pp. 1–11. vol. 28, 1992,pp. 1045–105. 680 | P a g e

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