Induction Motor Control Using Two Intelligent Algorithms Analysis

Size: px

Start display at page:

Download "Induction Motor Control Using Two Intelligent Algorithms Analysis"

Magnus Fleming
6 years ago
Views:

1 Inducton Motor Control Ung Two Intellgent Algorthm Analy Moulay Rachd Dour and Mohamed Cherkaou Mohammada Engneerng School, Department of Electrcal Engneerng, Avenue Ibn Sna, B.P.765, Agdal-Rabat, Morocco Abtract. The man drawback of clacal drect torque control (C-DTC) are: the hgh torque and flux rpple and varable wtchng frequency. To overcome thee problem, two ntellgent control theore, namely fuzzy logc control (FLC) and neuro-fuzzy control (NFC) are ntroduced to replace the hytere comparator and lookup table of the C-DTC for nducton motor drve. The effectvene and feablty of the propoed approache have been demontrated through computer mulaton. A comparon tudy between the C-DTC, FL-DTC and NF-DTC ha been made n order to confrm the valdty of the propoed cheme. The uperorty of the NF-DTC ha been proved through comparatve mulaton reult. Keyword: drect torque control, fuzzy logc control, nducton motor drve, neuro-fuzzy control. 1 Introducton The drect torque control (DTC) wa propoed by M.Depenbrock [1] and I.Takahah [2]-[3] n The DTC an entrely dfferent approach to nducton motor control that wa developed to overcome feld orented control (FOC) relatvely poor tranent repone and relance on nducton motor parameter [4]-[5]-[6]. Clacal DTC a popular torque control method for nducton motor; therefore t wdely ued n the area of the EV motor control. Unfortunately the clacal DTC algorthm ha ome gnfcant lmtaton. It dffcult to dtnguh between mall and large varaton n reference value. Alo the varaton of flux and torque over one ector conderable [7]-[8]. Another problem that adaptng clacal DTC to the confne of a DSP amplng perod can gnfcantly deterorate t performance [9]. To overcome thee problem, two ntellgent control theore, ncludng fuzzy logc control (FLC) and neuro-fuzzy control (NFC) are ntroduced to replace the conventonal comparator and electon table of drect torque control for nducton motor drve. Fuzzy logc can deal wth vague concept whch have relatve degree of truth rather than jut the uual true or fale, t allow machne to perform job that n the pat requred a human beng' ablty to thnk and reaon [10]-[11]. Conventonal control ytem expre control content by ung control expreon uch a equaton or H. Papadopoulo et al. (Ed.): AIAI 2013, IFIP AICT 412, pp , IFIP Internatonal Federaton for Informaton Proceng 2013

2 226 M.R. Dour and M. Cherkaou logcal expreon. Th requre a huge amount of nformaton, and ome knd of control are dffcult or mpoble to model th way. Fuzzy logc control uually requre only one tenth or le of the nformaton requred by conventonal method [12]. Th alo aocated wth a hgh relablty and fat proceng peed. Fuzzy logc alo deal effectvely wth a non-lnear tme varyng ytem. There a rapdly growng nteret n the fuon of fuzzy ytem and neural network to obtan the advantage of both method whle avodng ther ndvdual drawback. The poblty of ntegraton of thee two paradgm ha gven re to a rapdly emergng feld of fuzzy neural network. There are two dtnctve approache for fuzzy-neural ntegraton. On the one hand, many paradgm that have been propoed mply vew a fuzzy-neural ytem a any ordnary multlayered feed-forward neural network whch degned to approxmate a fuzzy control algorthm [13]-[14]. On the other hand, there are thoe approache whch am to realze the proce of fuzzy reaonng and nference through the tructure of a connectont network [15]. Fuzzyneural network are, n general, neural network whoe node have 'localzed feld' whch can be compared wth fuzzy rule and whoe connecton weght can mlarly be equated to nput or output memberhp functon. The mplet attempt n mergng of fuzzy logc and neural controller to make the neural network (NN) learn the nput-output charactertc of a fuzzy controller [16]. The NN n th cae mtate the fuzzy controller but the only advantage that the traned NN output ha more moothng robut acton than that of the fuzzy controller. Th paper organzed a follow: The prncple of drect torque control preented n the econd part, the fuzzy logc drect torque control developed n the thrd ecton, ecton four preent a neuro-fuzzy drect torque control, and the ffth part devoted to llutrate the mulaton performance of th control trategy, a concluon and reference lt at the end. 2 Clacal Drect Torque Control In a C-DTC motor drve, the machne torque and flux lnkage are controlled drectly wthout a current control. The prncple of C-DTC can be explaned by lookng at the followng torque and current equaton of an nducton motor: 3 Te = npim 2 { λ } 2 1 Lm Lm = λ λr, wth σ 1 σl σl L = L L r r (1) (2) Subttutng Eq. (1) n Eq. (2) we obtan: T 3 = n 2 L m n σ LL λ λ α e p r r (3)

3 Inducton Motor Control Ung Two Intellgent Algorthm Analy 227 where α the angle between the tator and rotor flux lnkage vector [8]. The dervatve of Eq. (3) can be repreented approxmately a: dte 3 L m dα = n p λ λr coα dt 2 σ L L dt r The machne voltage equaton can be repreented and approxmated n a hort nterval of Δt a: d λ = v R v mplyng Δ λ = v Δt (5) dt The rotor flux lnkage vector luggh n repone to a voltage vector durng Δt a t related to the tator flux lnkage vector by a frt order delay a n dλ Lm R r RrL m + jωr λr = λ dt σllr Lr σllr Symbol: R, R r tator and rotor retance [Ω] d, q tator current dq ax [A] v d, v q tator voltage dq ax [V] L, L r tator and rotor elf nductance [H] L m mutual nductance [H] λ d, λ q dq tator flux [Wb] λ rd, λ rq dq rotor flux [Wb] T e electromagnetc torque [N.m] E Te electromagnetc torque error [N.m] E λ tator flux error [Wb] φ tator flux angle [rad] ω r rotor peed [rad/ec] J nerta moment [Kg.m 2 ] n p pole par σ leakage coeffcent (4) (6) 3 Fuzzy Logc Drect Torque Control The tructure of the wtchng table can be tranlated n the form of vague rule. Therefore, we can replace the wtchng table and hytere comparator by a fuzzy ytem whoe nput are the error on the flux and torque denoted E λ and E Te and the argument φ of the flux. The output beng the command gnal of the voltage nverter n. The fuzzne character of th ytem allow flexblty n the choce of fuzzy et of nput and the capacty to ntroduce knowledge of the human expert.

4 228 M.R. Dour and M. Cherkaou The th rule R can be expreed a: R : f E Te A, E λ B, and φ E, then n N (7) where A, B and C denote the fuzzy ubet and N a fuzzy ngleton et. The ynthezed voltage vector n denoted by t three component the output of the controller. The nference method ued n th paper Mamdan [18] procedure baed on mn-max decon [19]. The frng trength η, for th rule gven by: ( E E λ ) η mn μ ( ), μ ( ), μ ( ϕ) = (8) A T B C e By fuzzy reaonng, Mamdan mnmum procedure gve: μ ( η μ n ) ( n) mn, ( ) ' N N = (9) where μ A, μ B, μ C, and μ N are memberhp functon of et A, B, C and N of the varable E Te, E λ, φ and n, repectvely. Thu, the memberhp functon μ N of the output n gven by: ( μ n ) μ ( n) max ( ) = (10) 72 ' N = 1 N We choe to hare the unvere of dcoure of the tator flux error nto two fuzzy et, that of electromagnetc torque error n fve and fnally for the flux argument nto even fuzzy et. However the number of memberhp functon (fuzzy et) for each varable can be ncreaed and therefore the accuracy mproved. All the memberhp functon of fuzzy controller are gven n Fg. 1. μ( ϕ ) 1 ϕ ϕ1 ϕ2 ϕ3 4 ϕ ϕ 5 6 ϕ1 μ( E Te ) 1 π 2π π 4π 5π π ϕ ( rad ) ET e ( Nm) μ( E λ ) E ( Wb) λ Fg. 1. Memberhp functon for fuzzy logc controller

5 Inducton Motor Control Ung Two Intellgent Algorthm Analy 229 Table 1. Fuzzy rule E E φ 1 φ 2 φ 3 φ 4 φ 5 φ 6 φ λ Te P V 6 V 2 V 3 V 1 V 5 V 4 PL Z V 4 V 6 V 2 V 3 V 1 V 5 N V 5 V 4 V 6 V 2 V 3 V 1 P V 6 V 2 V 3 V 1 V 5 V 4 PS Z V 7 V 0 V 7 V 0 V 0 V 0 N V 5 V 4 V 6 V 2 V 3 V 1 P V 2 V 3 V 1 V 5 V 4 V 6 NS Z V 0 V 7 V 0 V 7 V 0 V 7 N V 1 V 5 V 4 V 6 V 2 V 3 P V 2 V 3 V 1 V 5 V 4 V 6 NL Z V 3 V 1 V 5 V 4 V 6 V 2 N V 1 V 5 V 4 V 6 V 2 V 3 4 Neuro-Fuzzy Drect Torque Control In th ecton, the Neuro-Fuzzy (NF) model bult ung the multlayer fuzzy neural network hown n Fg.1. The controller ha a total of fve layer a propoed by Ln and Lee [17], wth two nput (tator flux error E ψ, electromagnetc torque error E Te ) and a ngle output (voltage pace vector) condered here for convenence. Conequently, there are two node n layer 1 and one node n layer 5. Node n layer 1 are nput node that drectly tranmt nput gnal to the next layer. The layer 5 the output layer. The node n layer 2 and 4 are term node and they act a memberhp functon to expre the nput/output fuzzy lngutc varable. A bell-haped functon adopted to repreent a memberhp functon, n whch the mean value p and the varance χ are adjuted through the learnng proce. The two fuzzy et of the frt and the econd nput varable cont of k 1 and k 2 lngutc term, repectvely. The lngutc term are numbered n decendng order n the term node; hence, k l +k 2 node and n 3 node are ncluded n layer 2 and 4, repectvely, to ndcate the nput/output Lngutc varable. Layer 1: Each node n th layer perform a MF: y 2 x c = μ ( x ) = exp a 1 A where x the nput of node, A lngutc label aocated wth th node and (a, b, c ) the parameter et of the bell-haped MF. y 1 pecfe the degree to whch the gven nput belong to the lngutc label A, wth maxmum equal 1 and mnmum equal to 0. A the value of thee parameter change, the bell-haped functon vare accordngly, thu exhbtng varou form of memberhp functon. In b (11)

6 230 M.R. Dour and M. Cherkaou fact, any contnuou and pecewe dfferentable functon, uch a trapezodal or trangular memberhp functon, are alo qualfed canddate for node functon n th layer. Layer 2 - Every node n th layer repreent the frng trength of the rule. Hence, the node perform the fuzzy AND operaton: ( μ λ λ μ μ ϕ ϕ ) y = w = mn ( E ), ( E ), ( ) (12) 2 A BTe Te C Layer 3 - The node of th layer calculate the normalzed frng trength of each rule: y w = w = (13) w 3 n Layer 4 - Output of each node n th layer the weghted conequent part of the rule table: = 1 ( λ ϕ ) y = w f = w p E + q E + m + n (14) 4 Te where w the output of layer 3, and {p, q, m, n } the parameter et. Whch determne the th component of vector dered voltage. By multplyng weght y by voltage contnuou V de of the nverter accordng to Eq. (15): V = y V (15) Layer 5 - The ngle node n th layer compute the overall output a the ummaton of all ncomng gnal: y 5 n w 1 f = = (16) Whch determne the vector reference voltage v (ee Fg. 4), from Eq. (17): v 9 = 1 = (17) j y Ve ξ The angle ξ obtaned from the actual angle of tator flux φ and angle ncrement dφ gven by th Eq. (18): ξ = ϕ + dϕ (18) y ( = 1..9) are the output gnal order of the thrd layer repectvely.

7 Inducton Motor Control Ung Two Intellgent Algorthm Analy 231 Δϕ w 1 y 1 w 2 y 2 w λ w 3 y 3 V E λ w 4 y 4 w 5 y 5 w 6 y 6 E Te w Te w 7 y 7 ζ w 8 y 8 w 9 y 9 V ϕ Fg. 2. Topology of the neuro-fuzzy model ued Table 2. Parameter ettng for ANFIS model ANFIS Settng Detal Input varable Electromagnetc torque error, and tator flux error Output repone Space voltage vector Type of nput MF Generalzed Bell MF Number of MF 2,3, 4 and 5 Type of output MF Lnear and contant Type nference Lnear Sugeno Optmzaton Method Hybrd of the leat-quare and the back propagaton gradent decent method. Number of data 520 Epoch 1000

8 232 M.R. Dour and M. Cherkaou 5 Smulaton Reult To compare and verfy the propoed technque n th paper, a dgtal mulaton baed on Matlab/Smulnk program wth a Fuzzy Logc Toolbox and ANFIS Toolbox ued to mulate the NF-DTC and FL-DTC, a hown n Fg. 3. The block dagram of a C-DTC/FL-DTC/NF-DTC controlled nducton motor drve fed by a 2-level nverter hown n Fg. 3. The nducton motor ued for the mulaton tude ha the followng parameter: Rated power = 7.5kW, Rated voltage = 220V, Rated frequency = 60Hz, R r = 0.17Ω, R = 0.15Ω, L r = 0.035H, L = 0.035H, L m = H, J = 0.14kg.m 2. Fg. 4(a), 4(b) and 4(c) how the torque repone of the C-DTC, FL-DTC and NF- DTC repectvely wth a torque reference of [ ]Nm. Whle Fg. 4(a ), 4(b ) and 4(c ) how the flux repone of the C-DTC, FL-DTC and NF-DTC repectvely wth a tator flux reference of 1Wb. na, nb, nc + V dc C λ ϕ E λ λ λ v C Te E Te T e T e ω r ω r Fg. 3. General confguraton of C-DTC/FL-DTC/NF-DTC cheme Table 3 repreent the comparatve reult n both tator flux and torque rpple percentage for C-DTC, FL-DTC and NF-DTC. The teady tate repone for the torque n NF-DTC fater and provded more accuracy compared to other control tratege preented n th paper. Table 3. Comparatve tudy of C-DTC, FL-DTC and NF-DTC Control tratege Torque rpple (%) Flux rpple (%) Re tme (ec) Settng tme (ec) C-DTC FL-DTC NF-DTC

Inducton Motor Control Ung Two Intellgent Algorthm Analy 233 a a b b c c Fg. 4.

NF-DTC repectvely 6 Concluon Two varou ntellgent torque control cheme worth knowng fuzzy logc drect torque control, and neuro-fuzzy drect

A better precon n the torque and flux repone wa acheved wth the NF-DTC method wth greatly reduce the executon tme of the controller; hence the

9 Inducton Motor Control Ung Two Intellgent Algorthm Analy 233 a a b b c c Fg. 4. (a), (b) and (c) torque repone of C-DTC, FL-DTC and NF-DTC repectvely, (a ), (b ) and (c ) Stator flux trajectory repone of C-DTC, FL-DTC and NF-DTC repectvely 6 Concluon Two varou ntellgent torque control cheme worth knowng fuzzy logc drect torque control, and neuro-fuzzy drect torque control have been evaluated for nducton motor control and whch have been compared wth the conventonal drect torque control technque. A better precon n the torque and flux repone wa acheved wth the NF-DTC method wth greatly reduce the executon tme of the controller; hence the teady-tate control error almot elmnated. The applcaton of neural network technque mplfe hardware mplementaton of drect torque control and t envaged that NF-DTC nducton motor drve wll gan wder acceptance n future.

10 234 M.R. Dour and M. Cherkaou Reference 1. Depenbrock, M.: Drect Self-Control (DSC) of Inverter-Fed Inducton Machne. IEEE Tranacton on Power Electronc 3(4), (1988) 2. Takahah, I., Noguch, T.: New Quck-Repone and Hgh-Effcency Control Strategy of an Inducton Motor. IEEE Tranacton on Indutry Applcaton IA 22(5), (1986) 3. Noguch, T., Yamamoto, M., Kondo, S., Takahah, I.: Enlargng Swtchng Frequency n Drect Torque-Controlled Inverter by Mean of Dtherng. IEEE Tranacton on Indutry Applcaton 35(6), (1999) 4. Caade, D., Profumo, F., Serra, G., Tan, A.: FOC and DTC: Two Vable Scheme for Inducton Motor Torque Control. IEEE Tranacton on Power Electronc 17(5), (2002) 5. Le-Huy, H.: Comparon of Feld-Orented Control and Drect Torque Control for Inducton Motor Drve. In: Conference Record - IAS Annual Meetng, vol. 2, pp IEEE Indutry Applcaton Socety (1999) 6. Vaez-Zadeh, S., Jalal, E.: Combned Vector Control and Drect Torque Control Method for Hgh Performance Inducton Motor Drve. Energy Converon and Management 48(12), (2007) 7. La, Y.-S., Chen, J.-H.: A New Approach to Drect Torque Control of Inducton Motor Drve for Contant Inverter Swtchng Frequency and Torque Rpple Reducton. IEEE Tranacton on Energy Converon 16(3), (2001) 8. We, X., Chen, D., Zhao, C.: Mnmzaton of Torque Rpple of Drect-Torque Controlled Inducton Machne by Improved Dcrete Space Vector Modulaton. Electrc Power Sytem Reearch 72(2), (2004) 9. Shyu, K.-K., Ln, J.-K., Pham, V.-T., Yang, M.-J., Wang, T.-W.: Global Mnmum Torque Rpple Degn for Drect Torque Control of Inducton Motor Drve. IEEE Tranacton on Indutral Electronc 57(9), art. no , (2010) 10. Andrew, P.B.: An Introducton to Mathematcal Logc and Type Theory: To Truth Through Proof. Kluwer, Dordrecht (2002) 11. Novák, V.: Reaonng About Mathematcal Fuzzy Logc and t Future. Fuzzy Set and Sytem 192, (2012) 12. Verbruggen, H.B., Babuška, R.: Fuzzy Logc Control: Advance n Applcaton. World Scentfc Publhng Co Pte Ltd. (1999) 13. Wang, L.-X., Mendel, J.M.: Fuzzy Ba Functon, Unveral Approxmaton, and Orthogonal Leat-Square Learnng. IEEE Tranacton on Neural Network 3(5), (1992) 14. Hu, C.-H., Ln, P.-Z., Lee, T.-T., Wang, C.-H.: Adaptve Aymmetrc Fuzzy Neural Network Controller Degn va Network Structurng adaptaton. Fuzzy Set and Sytem 159(20), (2008) 15. Takag, H., Suzuk, N., Koda, T., Kojma, Y.: Neural Network Degned on Approxmate Reaonng Archtecture and Ther Applcaton. IEEE Tranacton on Neural Network 3(5), (1992) 16. Nauck, D., Klawonn, F., Krue, R.: Foundaton of Neuro-Fuzzy Sytem, 1t edn. John Wley (1997) 17. Ln, C.-T., Lee, C.S.G.: Neural-Network-Baed Fuzzy Logc Control and Decon Sytem. IEEE Tranacton on Computer 40(12), (1991)

An adaptive high speed PMSM control for electric vehicle application

An adaptive high speed PMSM control for electric vehicle application An adaptve hgh peed PMSM control for electrc vehcle applcaton Flah Aymen Natonal School of Engneerng of Gabe flahaymenng@yahoo.fr Sbta aaâd Natonal School of Engneerng of Gabe Abtract: Th paper deal wth