APPLICATION OF VECTOR CONTROL METHOD FOR DEVELOPING ANFIS OBSERVER AS SPEED SENSOR FOR INDUCTION MOTOR SPEED CONTROL IN ELECTRIC VEHICLE

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1 Academic Reseach Intenational Vol. 2, No., Januay 202 APPLICATION OF VECTOR CONTROL METHOD FOR DEVELOPING ANFIS OBSERVER AS SPEED SENSOR FOR INDUCTION MOTOR SPEED CONTROL IN ELECTRIC VEHICLE Ea Puwanto Electonic Engineeing Polytechnic, Institute of Suabaya ITS INDONESIA Pof. D. I Soebagio Seuluh Novembe Institute of Tecnology, Suabaya INDONESIA soebagio@ee.its.ac.id Pof. D. I. Mauidhi Hey Seuluh Novembe Institute of Tecnology, Suabaya INDONESIA hey@ee.its.ac.id ABSTRACT Thee is a gowing inteest in electic vehicles due to envionmental concens. Recent effots ae diected towad develoing an imoved oulsion system fo electic vehicles alications. This ae is aimed at develoing the system design hilosohies of ANFIS obseve as seed senso.now the induction moto is gowing u to use as oulsion in the electic vehicles. The simle method to develo obseve fo detects the seed of Induction moto as oulsion in the electic vehicle is oosed in this ae, vecto contol method is one s of the method which develoing in this ae. Diect field-oiented induction moto dive system need oto flu obseve and oto angula seed identifie. ANFIS is used fo identifying aamete dynamics and system vaiable estimation, linea eithe non-linea. ANFIS with back oagation leaning algoithm has alied to estimate flu oto and identify oto angula seed of thee-hase induction moto. The simulation esult is found well fo the seed u to 200 m, and no good esult fo the seed less than 200 m. Thee is a gowing inteest in electic vehicles due to envionmental concens. Recent effots ae diected towad develoing an imoved oulsion system fo electic vehicles alications. This ae is aimed at develoing the system design hilosohies of ANFIS obseve as seed senso. Now the induction moto is gowing u to use as oulsion in the electic vehicles. The simle method to develo obseve fo detects the seed of Induction moto as oulsion in the electic vehicles ae oosed in this ae, diect field-oiented is one s of the method which develoing in this ae. Diect fieldoiented induction moto dive system need oto flu obseve and oto angula seed identifie. ANFIS is used fo identifying aamete dynamics and system vaiable estimation, linea eithe nonlinea. ANFIS with back oagation leaning algoithm has alied to estimate flu oto and identify oto angula seed of thee-hase induction moto. The simulation esult is found well fo the seed u to 200 m and no good esult fo the seed less than 200 m. Keywods: Obseve, Vecto contol, seed senso obseve, ANFIS INTRODUCTION Automobile manufactues ae actively studying ossibilities of electic vehicles fo widesead actical use. Howeve, thee ae a vaiety of oblems that must be solved fo electic vehicles, one s of the oblem is how to measue the seed of induction moto as oulsion. The contol system fo induction moto has the oblem fo uncetainly aametes, fo this case consideed the contolle is known stuctue and uses the values of the state vaiable. Howeve, this causes a new oblem, coes easily satuate when the imay cuents flowing in the winding ae inceased, because the imay and seconday ion coe sizes fo the motos must be made smalle. This leads to difficulties in accuate and stable toque contol of the moto in egions whee the ion coes of the moto is satuated. But some state vaiables cannot be measued, such as oto flu, to solving this oblem, the obseve to estimate the state vaiable that cannot be measued is needed. A flu obseve based on ANFIS will be used to estimate the flu, fo identifying moto induction angula seed. Automobile manufactues ae actively studying ossibilities of electic vehicles fo widesead actical use. Coyight 202 SAVAP Intenational

2 Academic Reseach Intenational Vol. 2, No., Januay 202 Howeve, thee is a vaiety of oblems that must be solved fo electic vehicles, one s of the oblem is how to measue the seed of induction moto as oulsion. The contol system fo induction moto has the oblem fo uncetainly aametes, fo this case consideed the contolle is known stuctue and uses the values of the state vaiable. Howeve, this causes a new oblem; coes easily satuate when the imay cuents flowing in the winding ae inceased, because the imay and seconday ion coe sizes fo the motos must be made smalle. This leads to difficulties in accuate and stable toque contol of the moto in egions whee the ion coes of the moto is satuated. But some state vaiables cannot be measued, such as oto flu, to solving this oblem, the obseve to estimate the state vaiable that cannot be measued is needed. A flu obseve based on ANFIS will be used to estimate the flu, fo identifying moto induction angula seed. The state equations fo induction moto ae develoed by using dq model, and it can be eessed as following equation in the stationay efeence fame. d is A A2 is B () = vs dt A2 A 22 0 λ λ = A Bv s i s = C (2) Whee X= i = s λ = v s A A2 A2 A, B= B 22 0 [ i ] T sd i sq [ λ ] T d λq [ v v ] T : Stato cuent : Roto flu = : Stato voltage sd sq = { R s ( σ Ls ) ( σ ) ( στ )} I = a I = M τ L L τ I ω I = a I a A A2 ( s ) ( ) 2 i2 { } J = ( M τ ) I = a I A 22 = ( τ ) I ω J = a 22I ai22j = ( τ L s ) I = I [ I 0] A 2 2 B b C = I = 0 0 J = 0 0 R s, R : Stato and oto esistance L s, L : Stato and oto self inductance M : Mutual inductance σ : Leakage coefficient, σ = M 2 L s L τ : Roto time constant, τ = L / R : Moto angula seed ω THEORY AND SYSTEM DESIGN A. Adative Netwoks of ANFIS System An adative netwok is a multilaye feed fowad netwok in which each node efoms a aticula function (node function) on incoming signals using a set of aametes secific to this node [3]. The fom of node functions may vay fom node to node, and the choice of each node function deends on the oveall function, which the adative netwok is designed to imlement. To eflect diffeent caabilities, both cicle and squae nodes in an adative netwok is used. A squae node (adative node) has modifiable aametes while a cicle node (fied node) has none. The aamete set of an adative netwok is the union of the aamete sets of each adative node. In ode to achieve a desied Coyight 202 SAVAP Intenational

3 Academic Reseach Intenational Vol. 2, No., Januay 202 inut-outut maing, these aametes ae udated accoding to given taining data and a gadientbased udate ocedue descibed below. Suose that a given adative netwok has L layes and laye l (l = 0,,...L; l = 0 eesents the inut laye) has N(l) node. Then the outut and function of node i (i =,...,N(l)) in laye l can be eesented as l,i and f l,i, as shown in the figue. Since the outut of a node deends on the incoming signals and the aamete set of the node, we have the following geneal eession fo the node function: l,i = f l,i ( l-,,..., l-i,n(l), α, β, γ,...) (4) α,β,γ ae the aametes of this node. Assuming that the given taining data set has P enties, we can define an eo measue fo the th (< = < = P) enty of the taining data as the sum of squaed eo : E = N(l ) 2 ( d L, k ) k k = whee d k : the kth comonent of the th desied outut vecto, L,k : the kth comonent of the actual outut vecto oduced by esenting the th inut vecto to the netwok. Fo notational simlicity, we omit the subscit fo both d k and L,k ) Thus ou task hee is to minimize an oveall eo measue, which defined as P = = E (6) E (5) The eo signal node i in laye l, ε l, i = ε l, i Figue. Feed fowad adative netwok is defined as the deivative of the eo measue E with esect to the outut of E l, i This eession was called the odeed deivative by Webos. The eo signal fo the ith outut node at laye L could be calculated diectly (7) ε l, i = E L, i = E L, i (8) Fo the intenal node at the ith osition of laye l, the eo signal can be deived by the chain ule ε l, i E = = l, i E f N(l ) N(l ) m= l,m = εl,m l,m l, i m= f l,m l, i (9) Whee 0 <= l <= L- Fo any l and i (0 < = l < = L and < = i < = N(l)) the eo signal can be found by fist alying Equation (8) once to get eo signals at the outut laye, and then alying Equation (9) iteatively Coyight 202 SAVAP Intenational

4 Academic Reseach Intenational Vol. 2, No., Januay 202 until we each the desied laye l. The undelying ocedue is called back oagation since the eo signals ae obtained sequentially fom the outut laye back to the inut laye. The gadient vecto is defined as the deivative of the eo measue with esect of each aamete. If α is a aamete of the ith node at laye l, E E fl,i f l,i = = ε (0) l,i α α α l,i The deivative of the oveall eo measue E with esect to α P E E () = α = α The udate fomula fo the geneic aamete α α = η α E (2) Whee η is the leaning ate, which can be futhe eessed as η = κ 2 E α α whee κ is the ste size, the length of each tansition along the gadient diection in the aamete sace. Usually we can change the ste size to vay the seed of convegence. B. Adative Neuo-Fuzzy Infeence Systems Adative Neuo-Fuzzy Infeence Systems (ANFIS) is a class of adative netwoks that ae functionally equivalent to fuzzy infeence systems. Stuctually, the only limitation on the netwok configuation is that it should be feed fowad tye if we do not want to use the moe comle asynchonously oeated model. The fuzzy infeence system unde consideation has two inuts and y, and one outut z is assumed. Fo a fist ode Sugeno fuzzy model, a common ule set with two fuzzy if-then ules is eessed such as the following model: (3) : If is A and y is B, then f = q y 2 : If is A 2 and y is B 2, then f 2 = 2 q 2 y 2 A A2 B2 B Y w f = q y wf f = w w2 = w f w 2f w2f2 2 w2 X y Y f 2 = 2 q 2 y 2 Figue 2. A two inut fist ode Sugeno fuzzy model with two ules Figue 2 illustates the easoning mechanism fo this Sugeno model, the coesonding equivalent ANFIS achitectue is as shown in figue 3 and the outut of the ith node in laye l as O l,i Coyight 202 SAVAP Intenational

5 Academic Reseach Intenational laye laye 2 laye3 laye 4 Vol. 2, No., Januay 202 laye 5 A A2 w y wf wi fi y B B2 w2 y w2f2 wi f Figue 3. Equivalent ANFIS achitectue Laye : Evey node i in this laye is an adative node with a node function Ο,ι = Αι(ξ) φ ορ ι =, 2, ορ Ο,ι = Βι 2(ψ), fo i = 3, 4 whee (o y) is the inut to the node i and A i (o B i-2 ) is a linguistic vaiable. In othe wods, O,i is the membeshi gade of a fuzzy set A (=A, A 2, B, B 2 ) and it secifies the degee to which the given inut (o y) satisfies the quantifie A. Hee the membeshi function fo A can be any aoiate aameteized membeshi function, such as Gaussian function: 2 i 2 a b i µ () = e (4) A whee {a i, b i } is the aamete set. As the values of thee aametes change, the Gaussian-shaed function vaies accodingly. Paametes in this laye ae efeed to as emise aametes. Laye 2 : Evey node on this laye is a fied node,whose outut is the oduct of all the incoming signals O 2,i = w i = Ai() Bi(y) i =, 2 (5) Each node outut eesents the fiing stength of a ule. Laye 3 : Evey node in this laye is a fied node labeled N. The ith node calculates the atio of the ith ule s fiing stength to the sum of all ule s fiing stengths O 3,i = w i f i = w i ( i q i y i ) (6) { i, q i, i } is the aamete set of this node, known as consequent aametes. Laye 4 : The fist node in this laye comutes the outut of laye 3, the second node below comutes the nomalized fiing stength of laye 2. Laye 5: The single node in this laye is a fied node, which comutes the oveall outut as the division of the incoming signals: i = wi fi = i i wf i i oveall outut = O5, (7) wi DESIGN AND EXPERIMENT The obseve was designed to estimate some induction moto oututs that can not be diectly measued. Obseve efomance was measued by the diffeence between induction moto outut and obseve. Moe less the diffeence then can be said that obseve aleady has high efomance. Taining data taken while the oveall system simulation unning without the obseve. The induction moto diven by PWM Invete and ootional seed contolle with gain=, at the set oint 250 Coyight 202 SAVAP Intenational

6 Academic Reseach Intenational Vol. 2, No., Januay 202 ad/s. Simulation un fo 5000 iteation, so we can get 5000 ais data. This data used fo find the fuzzy infeence system and the taining. A. Fuzzy Infeence System Design The Gaussian function is used in this simulation fo its smoothness, instead of tiangula shae. We do not have domain knowledge fom a human oeato s oint a view about the obseve, then the numbe of fuzzy if-then ules has to be decided by tial and eo. The flu obseve and angula seed identifie is imlemented as ANFIS with fou inuts on each. With subtactive clusteing method, we have eight membeshi function fo each inut, and eight ules fo each obseve and identifie. Though the numbe of fuzzy ules can be moe than eight, the simulation indicates eight ules ae enough fo identification. B. Taining To seed u the convegence, we follow a stict gadient descent in the sense that each tansition of the aametes will lead to a smalle eo measue. If the eo measue inceases afte aamete udate, we back u to the oiginal oint in the aamete sace and decease the cuent ste size by half. This ocess is eeated until the weight udate leads to a smalle eo measue. Howeve, this ste size udate ule tends to use a small ste size if the eo measue suface encounteed in the fist few udates is not smooth. Theefoe we multily the ste size by 4 afte obseving thee consecutive tansitions without any backu actions. The initial ste size in the simulation is 2 and the leaning ocess stos wheneve the numbe of eoch has given eached. As shown in the figue 4 is off-line configuation of the oto flu obseve, figue 5 fo oto angula seed identifie. On-line configuation at figue 6 shows us that estimated flu fom flu obseve make the inut fo oto angula seed identifie togethe with stato cuent. ω Induction Moto Model λ v s i s - e λ ANFIS Obseve () λˆ Figue 4. Taining scheme of obseve λ Induction Moto Model ω e ω i s ANFIS Identifie (2) ωˆ - Figue 5. Taining scheme of identifie v s ωˆ ANFIS Flu Obseve () ANFIS Seed Identifie (2) ωˆ λˆ i s Figue 6. Configuation of flu obseve and seed identifie at the simulation Coyight 202 SAVAP Intenational

7 Academic Reseach Intenational Vol. 2, No., Januay 202 RESULT AND DISCUSSION The validation stage, obseve and identifie is alied to connecting with induction moto, as shown the following figue 7. Time samling seconds, moto wee unning fo 0.5 seconds. ω ω K= im T e L M λˆ it vecto otation cosφ isd i sq sinφ cood. tans. 2/3 i a ib ic ia ib ic Switching PWM Invete va v b vc IM ˆ λ ˆ2 ˆ2 = λd λq cos ˆ φ = ˆ λ ˆ d λ sin ˆ φ = ˆ λ ˆ q λ λˆd λˆq ω ia ib ic Flu Obseve & Seed Calc. v a vb v c Figue 7. oveall system used fo simulation Paametes used fo obseve and identifie: Table. Paametes of obseve and identifie Paamete I II III Total Nodes of the inut laye Total Nodes of the st hidden laye Total Nodes of the 2 nd hidden laye Total Nodes of the outut laye Total Paametes Total Taining data ais Eoch The investigated simulation is done by using the ANFIS comosition which detemined in the table, the standad Eo Estimation (SEE) is found in the fist taining 200 eoch esults SEE diect oto flu Wb, SEE quadatu oto flu Wb, and SEE oto angula seed identifie ad/s, the gahic of simulation esult is shown in the fig 8 fo diect oto flu estimation, fig.9 fo the quadatue oto flu estimation and seed angula is shown in the Fig.0. Figue 8. Simulation esults fo diect oto flu estimation with taining 200 eochs Coyight 202 SAVAP Intenational

8 Academic Reseach Intenational Vol. 2, No., Januay 202 Figue 9. Simulation esults fo quadatue oto flu estimation with taining 200 eochs Figue 0 Simulation esults fo oto angula seed estimation with taining 200 eochs CONCLUSION The ANFIS obseve is comletely simulated fo identify the seed induction moto and the esults show that oto flu obseve and oto angula seed identifie can estimate flu oto and oto angula seed. Thus oto flu obseve and oto angula seed identifie shown a good efomance by using the back oagation leaning method.. The oblem is fo the seed unde 200 m, the obseve cannot estimate clealy. Rating and Paametes of Induction Moto Used fo Simulation 0 Watt, 5 Volt, 2 oles, 60 Hz R s = 76 [Ohm] R = 90 [Ohm] L s = [H] L = 3.3 [H] M= 3.2 [H] J = [kg.m 2 ] K d =.49e-5 [kg.m 2 / s] REFERENCES Boldea, I. (992), Vecto Contol of AC Dives, CRC Pess, Inc. Babuaj Kaanayil et al (2007). Online Stato and Roto Resistance Estimation Scheme Using Atificial Neual Netwoks fo Vecto Contolled Seed Sensoless Induction Moto Dive. IEEE Tansactions On Industial Electonics, VOL. 54, NO.. Geoge Ellis(nd). Obseve In Contol System A Pactical Guide, Academic Pess. Hisao K. and Kouki M. (994). Seed Sensoless Field Oiented Contol of Induction Moto with Roto Resistance Adatation. IEEE Tans. On Industial Alications, Vol. 30, No. 5. Coyight 202 SAVAP Intenational

9 Academic Reseach Intenational Vol. 2, No., Januay 202 Liu Xu, Ruan Yi, et al(2008). On Seed Sensoless Vecto Contol System fo Induction Moto Based on Estimating Seed by Toque Cuent Diffeential. Poceedings of the 27th Chinese Contol Confeence, Kunming,Yunnan, China Muat Baut, Student Membe, IEEE, et al(2007). Seed-Sensoless Estimation fo Induction Motos Using Etended Kalman Filtes. IEEE Tansactions On Industial Electonics, VOL. 54, NO.. M.N. Cistea, A. Dinu, J.G. Kho,M. McComick (nd). Neual and Fuzzy Logic Contol of Dives and Powe Systems, ewnes N.Ravi Sanka Reddy et al(200), Adative Neual Fuzzy Infeence Systems Contolle fo Hybid PWM based vecto contolled Induction moto dives, 6th National Powe Systems Confeence. Jang, J. S. R(992)., Self Leaning Fuzzy Contolles Based on Temoal Back Poagation, IEEE Tans. On Neual Netwoks, Vol.3, No. 5. Jang, J. S. R., C. T. Sun and E. Mizutani (997). Neuo-Fuzzy and Soft Comuting. Pentice-Hall Intenational, Inc. S.L. Chiu(994). Fuzzy Model Identification Based On Cluste Estimation. Jounal of Intelligent and Fuzzy Systems. Zhang Yongchang and Zhao Zhengming (2008). Seed Sensoless Contol fo Thee-Level Invete- Fed Induction Motos Using an Etended Luenbege Obseve. IEEE Vehicle Powe and Poulsion Confeence (VPPC). Coyight 202 SAVAP Intenational 5