Artificial Neural Network to Improve Speed Control of Permanent Magnet Synchronous Motor

Transcription

1 Proceeing o the 6th WSEAS/IASME Int. Con. on Electric Power Sytem, High Voltage, Electric Machine, Tenerie, Spain, December 16-18, Artiicial Neural Network to Improve Spee Control o Permanent Magnet Synchronou Motor A. NAIT SEGHIR 1 an M.S. BOUCHERIT 2 1 Faculté Electroniue et Inormatiue Univerité e Science et e la Technologie H.B. El-Alia, BP, N 32 Bab Ezzouar Algier, 16111, ALGERIA 2 Laboratoire e Commane e Proceu Ecole Nationale Polytechniue 10, Avenue Pateur, El-Harrach Algier, 16200, ALGERIA Abtract Thi paper preent the eign o a Neural Network bae approach to improve eiciency in a vector control o Permanent Magnet ynchronou Motor (PMSM). The conventional Proportional-Integral (PI) controller i largely ue in inutry becaue o the robutne thi regulator procure. But in ome cae, when the ynamic o the ytem vary over time or with operating conition, the perormance o the controller will be poile. The Artiicial Neural Network (ANN) ue a a pee controller eem to be a promiing olution in thi purpoe. In thi tuy we apply a multivariable (MIMO) neural network regulator in place o PI controller o the vector control cheme o the PMSM. Analyi an imulation reult are preente to emontrate the valiity o the propoe controller to enure robutne againt loa an parameter variation an to achieve the reuire perormance. Keywor Neural Network, Vector Control, Permanent Magnet ynchronou Motor. 1 Introuction Recent evelopment in power emiconuctor technology, igital electronic, magnetic material an control theory have enable moern ac motor rive to ace challenging high eiciency an high perormance reuirement in the inutrial ector. Due to it high eiciency, high power actor an robutne, the permanent magnet ynchronou motor (PMSM) ha been oten ue in high perormance application uch a robot an machine tool [1]. Uually high perormance motor rive ytem ue in thee omain reuire at an accurate pee repone, uick recovery o pee rom any iturbance an uncertaintie. Thi make the control o PMSM iicult at ierent ynamic operating conition. To achieve the bet ynamic behavior, the vector control metho i oten ue o that the PMSM can achieve the ynamic perormance capabilitie o the eparately excite DC machine, while retaining the general avantage o AC over DC motor [2]. The vector control i an eicient metho to control a ynchronou motor in ajutable pee rive application in wie range o pee. However, the perormance o preent metho i not goo enough. The conventional proportional-integral (PI), ue or the pee an the current in axi an, have ome limitation, a their eign epen on exact machine moel an accurate parameter. Moreover, the conventional ixe gain PI controller are very enitive to iturbance uch a parameter variation an loa iturbance [3]. Thee iicultie lea to cumberome the control eign approach. On the other han, the eign o intelligent controller o not nee the exact mathematical moel o the ytem. Due to there capabilitie to olving nonlinear problem by learning, Artiicial Neural Network are very ueul in thi area [4], [5]. In thi work, a neural network controller i evelope, or pee control o high perormance PMSM rive ytem, to improve eiciency o a vector control rive. Becaue o the electrical moel o PMSM i nonlinear two input two output ytem which nee a multivariable current controller, we propoe in thi paper a new multi input multi output (MIMO) neural network controller to replace both o the pee regulator an - axi current regulator. Firtly, the latter were eigne in conventional way. In the econ tep, an ANN-bae MIMO controller will be ue. Thu, the main objective o thi paper i to evelop an tuy an ANN controller that overcome nonlinear nature o PMSM an enure robutne o pee control againt parameter variation an loa iturbance.

2 Proceeing o the 6th WSEAS/IASME Int. Con. on Electric Power Sytem, High Voltage, Electric Machine, Tenerie, Spain, December 16-18, Filter PI Spee Regulator i =0 i Current control loop Decoupling v v Park -1 Tranlator PWM Inverter PMSM Encoer i i Park Tranlator i a ibic θ r Loa t Fig. 1. Vector control tructure o the PMSM rive Simulation reult are given to etablih the perormance an robutne o the propoe controller at ierent operating conition. ψ = rotor magnetic lux linking the tator. T he inverter reuency i relate a ollow : ω = Pω r 2 Motor Moel o PMSM The unaturate mathematical moel o a inuoial PMSM rive can be ecribe by the ollowing euation in a rotor - reerence rame a [1], [6]: v v ψ = Ri = Ri = L with: ψ = L i i + L + L + ψ i t t i L + L ω ω i i + ω ψ (1) (2) The mechanical euation i: ωr J m = Te TL m ωr (3) t In which the electromagnetic torue i given by: T = P (L L )i i + ψ i (4) e [ ] where, v, v =, -axi tator voltage; i, i =, -axi tator current; R = tator reitance; L, L =, -axi tator inuctance; T e, T L = electromagnetic an loa torue; J m = moment o inertia o the motor an loa; = riction coeicient o the motor; P = number o pole pair; = rotor pee in angular reuency; p = ierential operator (=/t); 3 Vector Control o a PMSM The baic principle in control o PMSM rive i bae on iel orientation. The lux poition can be etermine by the hat poition enor becaue the magnetic lux generate by permanent magnet i ixe in relation to the rotor hat poition [6], [7]. To enure the vector control o the PMSM, the techniue i =0 i the optimal trategy where the motor prouce the maximum torue. I i i orce to be zero by cloe loop control, then: ψ = (5) ψ an T = Pψ i (6) e Since ψ i contant, the electromagnetic torue i then irectly proportional to current i. The torue euation i imilar to that o eparate excite DC motor. It i evient rom euation (3) an (6) that the pee control can be achieve by controlling the -axi current component i a long a the -axi current i i maintaine at zero. Ater ecoupling the,-axi component i an i, the iel oriente PMSM rive cheme i hown in Fig Artiicial Neural Network Controller 4.1 Spee ANN controller The control objective aim to bring the motor pee ω to the eire pee reerence ω. r To replace the PI pee controller in Fig. 1, the new ANN controller mut ecribe the relationhip between r

3 Proceeing o the 6th WSEAS/IASME Int. Con. on Electric Power Sytem, High Voltage, Electric Machine, Tenerie, Spain, December 16-18, the -axi current i an the pee error Δωr = ωr ωr. Ater ome perorme tet, the mot convenient ANN architecture i proven to be a epicte in Fig. 2. Thi eeorwar network i tructure with our input, one hien layer with 5 neuron an one output. Thi imple tructure i choen in orer to reuce the memory pace an computational time. A nonlinear igmoi traner unction i ue in the hien layer an a linear traner unction i ue in the output layer. The input are the pee error in preent an previou two ample time, an previou ample time -axi current. The output target i the -axi reerence current. To train the eigne neural network, it i neceary to have enough et o input-output pattern. Since our aim i to obtain an ANN pee controller witch cover all the Δ Δ (k-1) Δ (k-2) i r (k-1) NN input Hien Layer Fig. 2. Architecture o the ANN (a) Mean Suare Error convergence (b) ANN Valiation tet Bia +1 Output neuron Fig. 3. Training neural network reult i r rive p ee range an operating conition, the appropriate input an output vector or the ANN are obtaine by the imulation o the vector control cheme o the PMSM rive uing the bet PI regulator or ierent operating point. The training proce i perorme o-line an accomplihe uing the Levenberg-Maruart back- i current an rotational propagation algorithm [5]. A tep reerence pee input i applie to the cloe loop ytem. During thi time, pee repone are collecte. Thi ata i then ue to train the neural network. The output o the network, repreenting the control input, i compare with the known PI regulator output an the error i ue to ajut the network weight [5]. The training proce i repreente in Fig.3 with the mean uare error (MSE) convergence an the valiation tet o the obtaine ANN. 4.2 ANN-bae MIMO controller The new multi input multi output bae ANN controller replace both o the pee regulator an - axi regulator in the claical vector control cheme. Thi controller can realize the multivariable control o the current - component an the rotor pee perormance goal. The tructure o the ANN-bae MIMO controller i choen ater ome perorme tet reerring to the perormance goal achieve with in MSE convergence an complexity o the network with repect to the number o computation an running time. The 3-layer neural network obtaine ater training an valiation tet have 8 input noe, 4 hien noe an 2 output noe a hown in Fig. 4. The igmoi traner unction i retaine or the hien layer an linear traner unction or the output layer. The ame training algorithm ue in the imple ANN controller i ue in thi cae. 5 Reult an Dicuion The parameter o the PMSM motor ue in thi work are given in Table 1. In orer to valiate the eiciency o the propoe ANN controller, the perormance o the PMSM rive bae on the propoe control cheme i invetigate in imulation at ierent operating conition. The complete controlle ytem ha been imulate uing Matlab/Simulink package. The imulate motor pee an -axi current component repone are preente. A reerence pee trajectory i generate uing a irt orer ilter to avoi a

4 Proceeing o the 6th WSEAS/IASME Int. Con. on Electric Power Sytem, High Voltage, Electric Machine, Tenerie, Spain, December 16-18, (k) i (k) U U -1-1 Fig. 4. Deign o the MIMO neural network controller Table 1 Machine Parameter Motor rate power 2 kw Rate pee 4000 r/min Pole pair number (P) 3 -axi inuctance, L 3,2 mh -axi inuctance, L 3,2 mh Stator reitance, R 1 Ω Motor inertia, J m kg m 2 Friction coeicient, m 9, Nm /ra Magnetic lux contant, ψ 0,39 Nm/A maximal current at the tarting. 5.1 Spee ANN controller In thi cae, we replace the PI pee regulator by a imple ANN controller with the tructure given in Fig. 2. In Fig. 5, the perormance o the conventional PI pee controller i tete. The reult how the pee repone to a reerence trajectory in preence o tep loa torue iturbance at 3 level o pee. The rive ytem begin rom tantill to the pee reerence et at 400 ra/. A contant loa tep o 5 Nm i applie at t=0.3 an remove at t=0.8. It can be een rom Fig. 5 that the rotor pee track the reerence trajectory with zero teay-tate error. The rotor pee oe not change uring the iturbance, while the current i reache it new value correponing to the applie loa. The tet o robutne o thi controller i given in Fig. 6, while a reitance parameter error i introuce (R=5R). The tet how the enitivity o the conventional PI controller to parametric perturbation. The ue o the ANN controller in pee reerence tracking with the preence o loa iturbance i hown in Fig. 7. The obtaine pee repone emontrate the perormance o the controller. t () Fig. 5. Spee reerence tracking repone in preence o loa iturbance uing PI controller. t () Fig. 6. Robutne againt error in reitance parameter (R=5R) uing PI controller. To tet the power o the propoe ANN, Fig. 8 an 9 give a comparion purpoe o the robutne o the conventional PI an the ANN controller with a parameter variation o a 5R error o tator reitance an 3J m error o inertia value. Thi tet inclue tep change in pee reerence an tep loa iturbance. We can

5 Proceeing o the 6th WSEAS/IASME Int. Con. on Electric Power Sytem, High Voltage, Electric Machine, Tenerie, Spain, December 16-18, ANN PI Fig. 8. Robutne againt 5R variation uing PI an ANN controller. t () Fig. 7. Spee reerence tracking repone in preence o loa iturbance uing ANN pee controller ee that the rejection o the loa iturbance with a mall variation i enure uing ANN controller. Thi how the capability o ANN controller to tart rom tantill conition to the rate pee a well a to reject the loa iturbance in preence o parameter variation. ANN PI Fig. 9. Robutne againt 3J m variation uing PI an ANN controller. 5.2 ANN-bae MIMO controller In thi ection, ANN-bae MIMO controller with the tructure given in Fig. 4, i ue or both o the pee an the - axi current control. Fig. 10 how the pee reerence tracking repone in preence o loa iturbance. Thi reult i nearly the ame a the previou neural controller. The ytem robutne againt 3J m variation i given in Fig. 11. A can be een, a mall overhoot in the pee repone appear but oe not aect the control uality in the ame way than in the previou control. The Fig. 12 preent robutne tet againt parameter variation. The 3 curve correpon to the normal conition tet, the inertia varying tet an the reitance varying tet. The tet reult emontrate that the obtaine ANN-bae MIMO controller meet iicultie to maintain the cot unction impoe in preence o parameter iturbance. Thi i ue to the complexity o the block replace by the MIMO neural network which nee more invetigation in term o input eature election an the ANN tructure. It i evient rom Fig that the propoe ANNbae MIMO pee controller i alo capable o t () Fig. 10. Spee reerence tracking repone in preence o loa iturbance uing ANN-bae MIMO controller. tracking the pee comman change an rejecting the loa iturbance.

6 Proceeing o the 6th WSEAS/IASME Int. Con. on Electric Power Sytem, High Voltage, Electric Machine, Tenerie, Spain, December 16-18, variation, it i uitable or real time application. Fig. 11. Robutne againt 3J m variation uing ANNbae MIMO controller. Fig. 12. Robutne againt parameter variation uing ANN-bae MIMO controller. Reerence [1] P. Pillay an R. Krihnan, Moeling, Simulation an Analyi o Permanent-Magnet Motor Drive, Part I: the Permanent-Magnet Motor Drive, IEEE Tran. on Inutry Application, Vol. 25, No. 2, March./April. 1989, pp [2] A. Boucayrol, T. Communal, Approche Globale e la Commane Dynamiue e Machine Electriue, Revue 3EI, No. 17, Juin. 1999, pp [3] F. J. Lin, Real-Time IP Poition Controller Deigne withtorue Feeorwar Control or PM Synchronou Motor, IEEE Tran. on Inutry Electronic, Vol. 44, No. 3, June. 1997, pp [4] J. M. Rener, Algorithme génétiue et réeaux e neurone, Application à la commane e proceu, Herme, Pari, [5] W. T. Miller, R. S. Sutton, an P. J. Werbo, Neural Network or Control, MIT Pre, [6] M. A. Rahman an M. A. Houe, On-Line Aaptive Artiicial Neural Network Bae Vector Control o permanent Magnet Synchronou Motor, IEEE Tran. on Energy Converion, Vol. 13, No. 4, December. 1998, pp [7] M. Solazzi an A. Uncini, On-line Quality Control o Permanent Magnet Motor Uing Neural Network, IEEE Tran. on Inutry Application, Vol. 36, No. 3, May/June. 2000, pp Concluion The application o the ANN in pee PMSM rive an the new ANN-bae MIMO controller to conuct the rive are coniere in thi paper. Firt, the neural controller wa applie in place o the PI pee regulator. The eectivene an ability o the propoe ANN controller ha been emontrate with imulation tet with regar to parameter an loa iturbance. In orer to veriy the eiciency o the ANN-bae controller, the imulation o the conventional PI regulator ha alo been carrie out uner the ame conition. The perormance improvement o the ANN controller over the conventional PI regulator i clearly hown in the imulation reult. The ANN-bae MIMO controller wa yntheize in orer to ubtitute the 3 PI conventional regulator in the pee PMSM control cheme. Very intereting perormance in pee reerence tracking with the preence o loa iturbance a well a time varying parameter are obtaine. Depite the act that the propoe controller i enitive to the tator reitance