Analysis of Neural and Fuzzy-power Electronic Control

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Analysis of neural and fuzzy-power electronic controlB.-R. Lin

Indexing terms Neural networks, Fuzzy-logic control, Inverter, UPS, DC-DC converter

Abstract: Current-controlled voltage-sourceinverters offer substantial advantages inimproving motor-system dynamics for high-performance AC-drive systems. The controllerswitches follow a set of reference currentwaveforms. Fixed-band hysteresis and sinusoidal-band hysteresis controllers have been studied.Neural network and fuzzy-logic-based current-controlled voltage-source inverters are developed.The models and learning techniques have beeninvestigated by simulation. The implementationof neural networks is described, and simulationresults are presented. The new UPS(uninterruptible power supply) with a fuzzy-logiccompensator is then proposed. The proposedfuzzy-logic compensator is used to preventvoltage drop from nonlinear loads. The totalharmonic distortion (THD) of the proposedscheme is better than that of the conventionaldeadbeat control method for linear and nonlinearloads. Finally, the application of fuzzy control toDC-DC converters, operating at finite switchingfrequency, is studied. Several control methodscurrently used for buck, boost and buckiboostconverters are compared to the fuzzy-convertercontrol. The fuzzy-logic and neural-networkcontroller for a unity power-factor rectifier arealso discussed. The simulations presented showthat the fuzzy-control method has better dynamicperformance and less steady-state error.

List of symbolsOp, i = the ith neuron output at the pth layerOp-l ,k= the kth neuron output at the (p- )th layerw p, j , k = the weight from the kth input of the (p - 1)th

layer to the ith neuron output of the pth layerA.) = logistic functionim,ref = m-phase reference sinusoidal currentlm =m-phase actual currentv r 6 = desired reference voltagevo = output voltage0 EE, 1997IEE Proceedirzgs online no. 19970516Paper first received 12th December 1995 and in revised form 19th March1996The author is with the Power Electronics Research Laboratory,Department of Electrical Engineering, National Yunlin Institute ofTechnology, 123 University Road, Section 3, Touliu City, Yunlin 640,Taiwan, Republic of China

iL = inductor currentv c =capacitor voltageX =state vectorA, =hysteresis current bandeP,, =the bias of the ith neuron at the pth layerE =a small error valueC I =variance of current errorAik =current error during kth intervalp A =membership function of fuzzy set Ap B =membership function of fuzzy set Bp(AuB)=membership function of the union A U B& n B ) =membership function of the intersection A nBp i =membership function of the complement of ap, =membership function of fuzzy relation RpBl =membership function of resulting output fuzzyAT(k) =pulse width at the kth sampling time1 IntroductionThe current-control technique has a most importantrole in current-regulated PWM inverters which arewidely used in AC motor drives, reactive power com-pensators and active power filters. The basic require-ments of such applications are low harmonics and fastresponse to provide the high dynamics of the system.There are two types of inverter, the voltage-sourceinverter (VSI) and the current-source inverter (CSI). Inthe voltage-source inverter, the DC input appears as aDC voltage source (ideally) to the inverter. For the cur-rent-source inverter, the DC input appears as a DCcurrent source (ideally) to the inverter. VSI drives gen-erally have superior variable speed performance to CSIdrives. The VSI drive has a fast response, low cost andpower-conditioning ability. If a high dynamic responseis required, the load current should be fedback andcompared directly with the reference value. Fast cur-rent control for AC motor drives with excellent tran-sient response can be realised with VSI drives. Acurrent-controlled PWM voltage-source inverter, whichcontrols the output current directly, provides high per-formance AC motor drives. Several current-controlstrategies have been proposed in recent years [l-31. Ahysteresis-band current controller [l ] has a simplestructure and the capability to limit the peak current.Hysteresis comparators select approximate inverteroutput voltage vectors based upon the output currenterror. However, the switching frequency of this methodis not fixed. To overcome this drawback, a constantswitching frequency predictive current controller [2],

fuzzy set A

set B' from basic inference rules

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that features an optimised performance in the steadystate, by systematically predicting the voltage vectorthat keeps the current in its hysteresis band, was pro-posed by Holtz. The problem with this scheme is thatcomplicated calculations are needed. Adaptive hystere-sis band current controllers were proposed in [3]. Thisscheme has the simple structure of the hysteresis-bandcurrent controller and the capability of current limiting,but the method of [3 ] is difficult to implement becauseof its complicated algorithm.

Presently, the uninterruptible power supply (UPS)has two basic topologies: off-line and on-line. All UPSapproaches use an internal battery that produces ACpower via an inverter. An off-line UPS is the simplestform of backup power. The inverter is normally off,and off-line UPS is also known as a standby powersource (SPS). The on-line UPS continuously convertsAC utility power to DC, by an AC-to-DC converter toprovide battery charging. The DC bus is supported bythe battery, and feeds a DC-to-AC inverter with appro-priate filtering. An on-line UPS provides the highestlevel of protection, since clean AC power is continu-ously supplied regardless of utility line condition. Theheart of the UPS system is the DC-to-AC inverter.There are several inverter control topologies [4] forsinusoidal output waveform applications. The draw-backs of most of these methods are high total har-monic distortion (THD) for nonlinear loads and poortransient response. The deadbeat-controlled PWMmethod [5] has a very fast response for load distur-bances and nonlinear loads. The disadvantage of thedeadbeat control method is high THD for nonlinearloads. The deadbeat-controlled PWM inverter, with acurrent-source compensator load, has low THD fornonlinear loads. The problem with this approach isthat additional hardware is needed which increases theUPS cost.

The modelling of networks which contain switcheshas drawn much attention, because of the unusualproperties of switches in comparison with other circuitelements. The difficulties in modelling switched net-works are mainly owing to the nonlinear and time-var-ying nature of switches. Most modelling in powerelectronics is intended to convert the nonlinear andtime-varying model to an ideal or non-ideal switchmodel [6]. Then, the state-space method is used to solvethe state equation for the system.

Neural network techniques have grown rapidly inrecent years. Extensive research has been carried out onthe application of artificial intelligence. Artificial neu-ral-network technology has the potential to provide animproved method of deriving non-linear models whichis complementary to conventional techniques. Neuralnetworks are intrinsically non-linear, and the actualalgorithmic relevant set of training examples is requiredwhich can be derived from operating plant data. Incontrast to other machine-learning techniques, neuralnetworks can modify their behaviour in response to theenvironment, have the flexibility of easily handling dif-ferent problem sizes, and have the potential for hard-ware implementation.

Fuzzy-set theory is a theory about vagueness anduncertainty. This theory provides an approximate, andyet effective, means of describing the behaviour of sys-tems which are too complex or ill-defined to permitprecise mathematical analysis. The fuzzy control is alsononlinear and adaptive in nature, which gives it a26

robust performance under parameter variations andload disturbances. Fuzzy-set theory uses qualitativetechniques instead of the conventional quantitative(numerical) techniques. Automatic control theory hasdeveloped from an empirically oriented technique,requiring precision well defined concepts and exactdata. Conventional control techniques provide goodperformance when the optimal control strategy isgiven. In modern control techniques, vagueness stilldoes play a role. The fuzzy control system providescontrol through a set of membership functions quanti-fied from ambiguous terms in the control rules. Fuzzycontrol features a short initial development period,because it can be implemented by a small number ofrules. The number of rules depends on the requiredaccuracy.

New power electronic control approaches, based onneural-network and fuzzy-logic techniques, are pre-sented in this paper. First, the current-controlled pulsewidth modulated (PWM) VSI based on the neural net-work method and fuzzy set theorem is presented. Sec-ond, the application of fuzzy logic to UPS systems forlow TH D is investigated. Third, the DC-DC convertercontrol using a fuzzy logic controller is studied.Finally, the artificial neural networks for the unitypower-factor rectifier are reviewed.2neural-network approach and a fuzzy logiccontroller

Current-controlled PWM VSI based on th e

The inverter AC motor drive has many advantagesover the conventional DC motor drive and, therefore,high-performance AC servo motor drives haveincreased in popularity. It is well known that precisecurrent control is a key technology in high-perform-ance AC drives. In the current hysteresis-controlledPWM, hysteresis comparators are used to impose adead band or hysteresis band in a small range from thereference current. The hysteresis control scheme pro-vides an excellent dynamic performance because it actsquickly. The disadvantage of this method is that theswitching frequency changes according to the operatingcondition of the motor. Recently, several researchershave used a sinusoidal-band hysteresis current control-ler, where the hysteresis bands vary sinusoidally over afundamental period rather than being fixed.

Fig.1 Structure of an elementary neuron

2. Ine ura -n et work approachThere are many artificial neural-network architecturesthat have been proposed. One architecture has beenpredominant; that is the feedforward neural network(FNN). The standard neuron structure, illustrated inFig. 1, is adopted, which is comprised of a summer and

Current-controlled PWM VSI based on

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a logistic functionf(net,J that can be either a sigmoidor a linear function. The equation for the ith neuron ofthe pth layer structure is

nnetp, , = W p , z , k O p - l +Q p, i (1)o p , z = ( n e t p , z ) ( 2 )

k = l

where Op,i s the output, Op-l,k is the kth output at the(p- 1)th layer, wp,i ,ks the weight from the kth input ofthe (p - 1)th layer to the ith output of the pth layer,and Op,i is the bias. These neurons are organised inlayers, as shown in Fig. 2. Scaled data enters thenetwork at neurons of the input layer, and ispropagated to the output through intermediate layers.Each connection has associated with it a weighting,which modifies the signal strength. There are varioustechniques for optimising criterion functions to trainthe neural network. One important characteristic ofneural-network classifiers is that their training usuallyrequires iterative techniques. The backpropagationclassifier is the most popular technique, trained byusing the gradient descent method. The advantage ofthis method is that it is simple and easy to understand,but the disadvantage is that the speed of convergence isslow. One way of overcoming these limitations is to usenumerical analysis and stochastic methods. The RLS(recursive least square) method [7] uses an extendedKalman estimation to optimise a function which is onlyknown through random samples. This approach can beapplied to linear or nonlinear problems. The power ofthe RLS is obtained at the price of a fairly largeamount of computation O(N2) or every update, whereN is the dimensionality of the input vector. Theconjugate gradient algorithm [8] optimises a criterionfunction, by assuming that it is locally quadratic. Thisalgorithm requires O ( N ) computations per iteration.Although the backpropagation classifier is not sorobust, it is easy to understand.

layer 1 2 L-1 L

Fig.2 Fee.dforwurd neural-network architecture

In the neural-network method, training is required tolearn something about the plant behaviour. The inputsto the neural network are three-phase current errors,and the outputs are the voltage vectors. Current errorscan be randomly generated at the neural-networkinput, and the backpropagation method is used toupdate the weights so as to decrease the current errors.Since the hysteresis current control for the voltage-source inverter (VSI) is known, the neural networkmust learn the dynamic behaviour of the hysteresis cur-rent control. It can be on-line or off-line learning.Fig. 3 shows the neural-network inverter control. Thethree sinusoidal reference currents are ia,refi $,ref, ic,ref.Reference currents ib,Yef and ic,,ef are phase shifted 120"and 240, respectively, from First, the three-phasecurrents from the VSI are measured and comparedIE E Proc.-Sci. Meas. Techno[., Vol. 144, No . 1. January 1997

with the three reference currents. The error signals aremultiplied by a given coefficient, G, and then input tothe neural network. The neural network is trained tohave minimum output error. The training rule isif li,,ref - Eif im,Tef < -Ewhere m =a , b or c, and 0 , is the output of the neuralnetwork. PS is the pulse separation circuit that willkeep two output waveforms of PS from overlapping. Ifthe two output waveforms from PS overlap, the upperand lower transistors in one inverter leg will conduct atthe same time, which will damage the transistors.Finally, as expected, the three current output wave-forms of the VSI will follow the three sinusoidal refer-ence currents.

=+keep 0, at the same state=+let 0, =1=+let 0 , =0(3)

'c,ref'b,refla,ref

layer layerhidden 'layerNeural-network current controlled inverter driveig .3G =gain; Ps =pulse separation

To perform a comparative evaluation of fixed-bandhysteresis, sinusoidal-band hysteresis and neural net-work methods, a simulation model is developed. Thesystem parameters in Fig. 3areDC bus voltage V =75 voltsInductance L =0.01HResistance R =2QSwitching frequency = 19.8kHz

Simulation results for the output current waveforms ofthe VSI for a change in frequency command are shownin Fig. 4.

0 100 200 300 400 500 600time * 1119800,sFig.4 Inverter out ut current wave orms for change in frequency com-mand based on neurafnetwork method

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reference

Fig.5 Closed-loopcontrol Uconfiguration2.2 Current -cont ro l led PWM VSI based onfuzzy log ic cont ro l le rThe fuzzy set theory, based on fuzzy sets and fuzzyalgorithms, provides a general method of expressinglinguistic rules so that they may be processed quicklyby a computer. At the same time, it is usually possiblefor an experienced operator to express the strategy orprotocol for control of a plant, using linguistic varia-bles, as a set of rules to be used in the different situa-tions. Fuzzy conditional statements are expressions ofthe form IF A is - hen B I S -. A fuzzy algorithm is anordered sequence of instructions which may containfuzzy assignment and conditioiial statements. The exe-cution of such instructions is related to the composi-tional rule of inference.Fig. 5 showy the basic closed loop fuzzy control con-figuration, Si n e the plant produces nonfuzzy measure-ments, these have to be fuzzified. Similarly, since theplant cannot respond directly to fuzzy controls thefuzzy control sets generated by the fuzzy algorithmhave to be defuzzified. The fuzzy control algorithmconsists of a set of fkzzy control rules which are relatedby the concepts of fuzzy implication and the composi-tional rule of inference. These fuzzy control rules arecombined by using the sentence connectives and andalso. A fuzzy variable is expressed by natural lan-guage. For example, the error of the output voltage canbe defined by linguistic variables zero (Z), positivesmall (PS), positive medium (PM), positive big (PB),negative small (NS), negative medium (NM), nega-tive big (NB), etc. The basic fuzzy set operations areunion. intersection and complement. Let A and Bbe two fuzzy sets in U with membership function p,and p B , respectively, where U is the universe of dis-course. The universe of discourse is a collection offuzzy variable possible values {x} The membershipfunction of the union A U B is max{pA(x),,uB(x)}. he menibership function ,qAnB)f the intersec-tion A nB is min{pA(x),pB(x)}.The membership func-tion p.1~ f the complen;cr!t of a fuzzy set A is 1 - pA(x).Normally, the fuzzy rule has the if .. then .. structure,such as

If x is L an d y is M then 2 s Nwhere x and y are input fuzzy variables, z is the outputfuzzy variable; L , M and N are fuzzy subsets in theuniverse of discourses X , Y and Z , respectively. A fuzzysubset can be NB, NM, NS, Z , PS, PM and PB. Ifthere are y1 fuzzy rules (R1 to Rn) , then individual rulesare combined by using the union operator.

positional operator is applied to infer the output fromthe given process states x, y and the fuzzy relation R.Table 1: Fuzzy control table showing change in controloutput

errorAerror N Z PN N N ZZ N Z PP Z P PN =negative, Z =zero, P =positiveA fuzzy relation R from A to B is a fuzzy subset of theCartesian product U x V, where A E U and B E V.The fuzzy relation R can be defined as follows:Once the relation R is defined, the actual input fuzzyset A can be used to compute the resulting outputfuzzy set B. There are two basic inference rules tocompute the resulting output fuzzy set B, max-minand max--product.

P R ( U , V ) =min(PUA(U),PU());UE U, E v (5 )

( 7 )The inference rule can be extended for any number offuzzy inputs and outputs. Since the control action zcannot directly control the plant, the defuzzificationoperation can be performed by the mean of maximumor centre of gravity method. The mean of maximumgenerates a control action, which represents the meanvalue of all local control actions whose membershipfunctions reach the maximum. In other words, if the setpossesses a single maximum, this value is the controlaction taken. If several local maxima appear in the set,then the control action to be executed is the meanvalue of these maxima. The control action is expressedas 5 z

( 8 )t= 1z , =_ _nwhere z , is the support value when its membershipreaches the maximum value, and n is the number ofsuch support values. The centroid defuzzificationmethod is uniquely determined from the fuzzy responseset R as follows

(continuous case) (9 1z 4 2 ) dxJ dz) zz , =R =R1U R2 U R3 U . . U R, (4 )

From fuzzy control rules, the corresponding controltable can be as shown in Table 1. For example, if theerror is Z and the change of error is P, then the changeof control output is P . A fuzzy controller should com-pute the actual output control signal for a specificinput signal from a given set of fuzzy rules. The com-

or2% Pc *(4cA(4z (discrete case) (10)o = 2

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Then the output signal zo is applied to control the con-verter.

The principal fuzzy-control algorithm is described asfollows:Step 1: Sample the output signal of the plantStep 2: Calculate the error and change of errorStep 3: Determine the fuzzy subset and membershipfunction for error and change of errorStep 4: Determine the change of control action accord-ing to the individual fuzzy ruleStep 5: Calculate the actual change of control action bydefuzzification operationStep 6: Send the change of control action to control theplantStep 7: Go to step 1

defuzzify fuzzify ehhT)

-1A- Ic,ref- 'b,ref- 'a,ref1

Fig.6 Fuzzy-logic current-controlled inverter drivesFig. 6 shows the fuzzy-logic current-controlledinverter drive. The three-phase currents from theinverter output are sensed and compared to the three-

phase reference currents. The error signals are multi-plied by the coefficient GE and fuzzified to fuzzy sets.Fuzzy subsets NB, NM, NS, Z, PS , PM and PB areused in fuzzification. According to the input fuzzyvariables, the fuzzy-logic controller determines theappropriate control output based on fuzzy rules. Thefuzzy rules used in this proposed scheme are shown inTable 2. The fuzzy control output cannot control theinverter directly. The defuzzification converts the fuzzydata into numeric data and output control action tothe inverter-based on the centre of gravity method. Thecoefficient GU can be used to adlust the output controlsignal from the defuzzify output.To perform an evaluation of current-controlledPW M VSI based on the fuzzy-logic method, a simula-tion model is developed. Assume the induction motorwas represented by three identical L-R circuits in threebalance output waveforms. The system parameters inFig. 6 are

DC bus voltage =75 voltsInductance L =O.OlHResistance R =2 QSwitching frequency = 19.8kHz

IE E Proc SCI e a $ Technol, Vol 144, N o I . January 1997

Table 2: Fuzzy control table showing change in controloutputerror

Aerror NB NM NS Z PS PM PBNB NB NB NB NB NM NS ZNM NB NB NB NM NS Z PSNS NB NB NM NS 2 PS PMZ NB NM NS Z PS PM PBPS NM NS Z PS PM PB PBPM NS Z PS PM PB PB PBPB Z PS PM PB PB PB PBNB = negative big, NM = negative medium, NS = negativesmall, Z =zero, PB =positive big, PM =positive medium, PS =positive smallSimulation results for the output current and PWMwaveform of the VSI controlled by the fuzzy-logicmethod are shown in Figs. 7 and 8 with the hysteresisband, Ai = 0.06A. In the proposed fuzzy-logic controlmethod, the peak to peak current error of phase A is0.5524A and the variance of current error is 0.1132A.

0 50 100 150 200 250 300time f 1119800,sFig.7currents Proposed fuzzy-logic current-controlled P W M VSI: three-phase

I I

.,.. . . . , . ..I

0 50 100 150 200 250 300time *1119800,sFig.8 Proposed fuzzy-logic current-controlled P WM VSI phase (IPW M3 UPS system wi th fuzzy-logic c ompensatorThe basic single-phase PW M inverter for the UPS con-sists of the single-phase full-bridge inverter, L-C filterand load. The controller controls the inverter switches

'L--.-LT - 1 vo

controller

Fig.9 Single-phase uzzy-inv erter control circuit

linearload ornonli neor

-

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so that the output voltage follows the reference sinusoi-dal waveform. The load voltage and inductor currentare defined as state variables. The appropriate pulsewidth can be computed in order to obtain the sinusoi-dal output voltage. The basic deadbeat control law [5]includes two parts. First, the state variable at the nextsampling instant is estimated. Second, the deadbeatcontroller controls the inverter switches so that the out-put voltage follows the reference sinusoidal waveform.In order to derive a discrete time model, the three-levelPWM pulse pattern [5] is considered. Based on themeasured signal at the (k 1)th sampling instant andthe estimated state variable at the kth sampling instant,the required pulse width AT@) can be computed,according to the deadbeat control law, to make theoutput voltage vo(k+ 1) equal to the desired referencevoltage v,,f(k+ 1) at t = (k + 1)T, where T is the sam-pling time.where a l , a2 and a3 are determined by the circuitparameters. The disadvantage of the deadbeat controllaw is the sensitivity to load variation.

AT@)=a1 0 U , , f ( k +I) +a2 0 wo(k) +a3 0 ZL(k) (11)

deadbeat control UP Sfuzzy-logiccompensator UP S\ 1

1

0.50

15 60 120 (V)

T H D comparisons or nonlinear load (firing angle =8S0)To overcome the deadbeat control-law drawback, theoutput-voltage control scheme with a fuzzy-logic com-pensator is proposed in Fig. 9. The circuit consists ofthe single-phase full-bridge inverter, L-C filter andload. This scheme measures the output voltage vo(t)

and the inductor current iL(t).To derive a discrete-timemodel, the PWM pulse pattern is considered with asample period T. The output voltage at the next timeinterval is predicted for real-time digital control. Inorder to compensate for the voltage drop for the non-linear load, the fuzzy-logic compensator is used. Theload voltage is sensed as the control variable for con-trolling the PWM pulse width. If a sudden change inload voltage is sensed, the fuzzy-logic controller willadjust the pulse width to compensate for the effect ofthe nonlinear load. First, the output-voltage error andchange of error are obtained by comparing the loadvoltage and reference voltage. The nonfuzzy variablesare changed to fuzzy variables by fuzzification. Thefuzzy controller compensates the PWM pulse widthaccording to the fuzzy rules. The defuzzificationchanges the fuzzy variables to a real control signal,Zo(k), or nonlinear load compensation. Eqn. 12 showsthat the next control PWM time interval AT(k +1) canbe obtained if v,(k), iL(k)and AT(k) are given.

output voltage Vo ,VFig.10

AT(k+1)= f l W v e f ( k+2) + 2 U , , f ( k +1)+ 3 0 2 L ( k )+ 4 % ( k )+f5 0 AT ( k )+& ( k ) (12)

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where vrer(k + 2) and vrer(k+ 1) are reference outputvoltages at t = (k + 2)T and ( k + 1)T, AT@) is thePWM time interval at t = kT. The proposed controlalgorithm was simulated by using MATLAB softwarerunning on a 80486 microprocessor. The following cir-cuit parameters were used in the simulation: Vdc=40V,L =0.5mH, C = 400mF, T = 50ms, R, = 3 Q (rated)and VFeJpeak15V. Since the UPS performance is eval-uated by total harmonic distortion, the comparisons ofthe proposed scheme and deadbeat control method areshown in Fig. 10. From the simulations, the proposedfuzzy-logic compensator has a better transient perform-ance, low total harmonic distortion for nonlinear loadsand robustness for circuit-parameter variations.4 DC-DC conv erter contro l4. I Fuzzy logic approachFig. 11 shows the boost converter system with a fuzzy-logic controller [9]. The actual output voltage Vo iscompared to the reference voltage VYe) o produce anerror signal that is used to determine the switch duty

L D'L

hl----c

R c +7 DC R

fuzzy+ ontrol- uzzlfyrules

Intel 80196MCdefuzzify e v r e dI

L _ - - - _ - _ - - - - - - - - - - - - - JAe(nT)I d(nT)Fig.11cycle. The state-space averaging method is very usefulfor analysing the low-frequency, small-signal perform-ance of switching circuits. It is applicable when thepower switching period is short compared to theresponse time of the output For subsequent discus-sions, the averaging method is used by considering thebucklboost converter with a second-order filter, andassuming continuous current in the inductor.Switch on:

Fuzzy logic boost converter control

Switch off=A~ * 5 +B~ *vDc

The average-state equation is then a weighted averageof the two state eqns. 13 and 14, based on the dutycycle ratio.$ =A* x+B *V& !A =d * Ai +(1 - d ) * A 2B = d * B 1 + ( 1 - d ) *Bz (15)IEE Proc.-Sci. Meas. Technol.,Vol. 144,No. I , Januavy 1997


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Then the small signal linearised behaviour of the boostconverter is used [lo] to solve the problem. Anotherapproach is to use difference equations for the mathe-matical models of discrete systems to solve the prob-lem. To reduce steady-state error and improve transientperformance, the switching duty cycle, d, is updated bya fuzzy-logic controller. The actual converter outputvoltage v, is compared to the reference voltage V,,, toproduce an error signal that is sent to the fuzzy-logiccontroller to determine the switch duty cycle. Figs. 12and 13 show the output-voltage transient behaviourwith a step input-voltage change and load change,respectively, in the boost converter. The experimentalresults show that the fuzzy-control method has a goodperformance. This control scheme can also be used inbuck and buck/boost and cuk converter control.

Fig.12changeV, =1 I V to 16V, V, =20 VFuzzy boost converter transient response showing input voltage

Fig.13changeio =1 A to 2A , V,, =20V

Fuzzy boost converter transient response showing output load

4.2 Neural-network approachFig. 14 shows the indirect NN control system with aseries-parallel model for a DC-DC converter. TheNN1 is used to learn and identify the nonlinear DC-DC converter behaviour and parameter variations. The 2 is used to generate a proper control signal inorder to force e2 to be as small as possible. Since theneural networks have learning capability, both NN1and 2 can automatically learn the dynamicIE E Proc.-Sei. Meas. Technol.,Vol. 144, N o. I , January 1997

characteristics of the DC-DC converters. Chan [111performed the NN DC-DC converter control system,which provided a good performance under high-frequency, a pulsed supply voltage and a referencesignal. From the discussions in [10-121, both the fuzzylogic and neural networks can learn andDC-DC converter dynamic characteristics. identify the

I -)e2

Fig. 14D =delay Indirect NN control systemfo r a DC-DC converter

Conduction mode : Circuit techniques:*continuous conduction *boostconverterdiscontinuo us conduc tion buck-boost conv erter*cukconverter-5yback converterzeta conver terControl mode :constant frequency control-constant tolerance-band control*vari able olerance-band control sepic converter

* discontinuous current controlFig. 15 Basic power factor correction control approachesm-10T

DC, ref DC.ref IFig.16 A N N unity power;factor control circuits5netwo rk controller and a fuzzy controller

Unity power-factor rectifier based on a neural-

Basically, unity power-factor AC-DC converters can berealised by several techniques. Fig. 15 shows the basiccontrol approaches. Recently, single-phase [131 andthree-phase [141 unity power-factor converters, basedon artificial neural networks (ANN) have beenproposed. Fig. 16 shows two basic ANN unity power-factor control circuits. The output voltage, accordingto the load demand, is regulated by the DC voltageloop. The amplitude of the current reference is variedby the voltage error and the PI controller. The currenterror between the actual line current and the currentreference is the input to the NN controller. After on-line NN learning, the input line current is waveshaped,and provides unity power-factor operation. Since the

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Analysis of Neural and Fuzzy-power Electronic Control