This paper describes an application of a Self Adaptive Pseudo-Moving Average Filter used in the implementation of a Smart Load Cell, to combine a stable digital output with a fast response to weight changes.
 The Smart Load Cell is a data acquisition solution using a single chip RISC microcontroller with very few other active and passive components around and taking advantage of the ratiometric functioning of load cell. The use of Smart Load Cells with digital outputs needs a cost effective in digital filtering of the final converter results for each Smart Load Cell. The technique is established by  theoretical analysis and is justified by means of simulation and experimental results.
The paper also describes an example of software calibration of a Multi-Load-Cells weighbridge, using four smart load cells.CEL-Cachapuz Electrónica Lda

Correia, J. H.

Couto, Carlos

Universidade do Minho: RepositoriUM

IE"CoN '3,;:; ,Or2-\A;NOO ,ç:'O(L:o~ I VS4-José HiginoDepartamento de Electrónica 1 ndustrialUniversidade do MinhoLg Paço, 4719 Braga codcxPORTUGALe-mail: jhc@)dei.umjnho.ptAb.Jtroct - Tbu p8per describes 8n 8ppliC8tion of 8Self-Ad8ptive Pseudl.-MovinR AvenRe Filter used in lheimplement8tlon of 8 Sm8rt Lo8d Cell. to combine 8 It8bledigit81 output witb 8 f8st response to weiRht ch8nRes.The Smart wad Cell il a data 8cquilition lolution ulinR alingle chip RISC microcontroller with very few other 8ctive8nd p8ssive componentl around and t8kinR adv8nt8Re of lheratiometric functioninR of load cello The ule of Smart wadCells witb diRit81 outputs nfedl 8 mlt effective in diJ;it81filterln~ of the fin81 converter results for e8ch Sm8rt Lo8dCell. The technique is establuhed by theoretical an8lYli~ 8nd "justified by mnns of simul8tion 8nd experiment81 results.Tbe p8per 81so describes an ex8mple of softw8re c81ibntionof 8 Multi-wad-Cells weiRhbrid~e. u~in~ four ..m8rt IO8d cellsoI. INTRODUCTIONLoad Cells have 1X1en uscx1 for many years in a variety ofapplications and are gcnerally considcred to be lhe forcesensor for industrial wcighting systems.The use of load ~lIs with digital outputs (I J, i.c., withintegrated signai processing was the objective to ooild onesinglc proccssing module pcr scnsor (Smart Load Ccll) [6].MODERN SOLUTIONDigital Filtering in Smart Load CellsCarlos CoutoDepartamento de Electrónica IndustrialUniversidade do MinhaLg Paço, 4719 Braga codcxPORTUGALe-mail: ccouto(il~dci.urninho.ptThc amplifier gain and ofTset adjustment controlled bysoftware. digital filtcring and nctwork facilitics nccdproccssing ca~bilitics. for lhat reason tbc use of thc highpcrfontlancc lo~' cosI microcontrollcrs availablc loday [4).Fig.1 shows thc conceptual idca of onc Smar1 Load Cell [8 J:Fig.1 Sm8r1lo8d CellFig. 2 shows a modcrn solution ror a Multi-Load-Cellsindustrial weighting system with a cable to travei over aliSmart Load Cells.,Lo8d Cellwith digit8r output,.- no summing box- simpler cabling and lessprone to interference- software calibration possible550The circuit block diagrarn in Fig.l shows: the amplifrerstage, singie ramp convcrsion, com.-rator block and lhemicroconlroller an 8 bit single chip Harvard archilecluremicroconlroller wilh RISC-like fealores, lhe PICI7C42, withinteresling characteristics for Ihis tYJx: of applicalions:- operating speed: DC - 20 MHz clock input (200nsinstnK:tion cycle).- Low cost.- Small size wilh EPROM.- Low power consumption.- Three 16-billimer/counters.Having in mind industrial weighting applications where6(XX> divisions are needOO (7) for thc equiprncnt (cxtcrnaldivisions) a conversion ~Iution of at least 6O()OO divisions(10 infernal divisions for cach extcrnal) with 50 or morereadings per secolKi, at lcast for static weighting appiications(3]. For dynamic weighting a fastcr rcading rale is requirOObut with lowcr ~Iution.The obscrvation of lhe signal from lhe load ccU reveals ahigh-fn';quency random osciUatory component ofher than lheosciUation doe to lhe lnd arder transient responsc nonnalwith load ceUs (lJ. Common sourccs of noisc are:electromagnetic pick-up, thennally unstable circuits and lhegajo programmable by software (through PWM's output's oftI.: microcontroller) 15]. This signal is amplificd ix:fore tl1Canalog digital conversion, therefore lhe noise is amplifiedand an error is intnxluced in lhe systcm.Adaptive low pass filtcring is naxkxI to providc a stablcrcading of the weight - largc time constant - as well as lheability to foUow, as quickly as possible, changes in lhewcight - short timc constant.Analog filtering would be complex and also would nottake care of lhe conversion noise, lcaving the software digitalfiltering as lhe OOvious olution.11. SELF-ADAPTIVE PSEUDO-MOVING A VERAGEFIL TER (SAPMAF)The simplest methOO of eliminatjng lhe high f~ucncynoise is to use a single first-ordcr low-pass filter. An analogsolution wouid be a standard configuration of a resistor Rand a capacitor C (a timc constant t=RC). Thc larger valueof t lhe slower lhe static valuc will rcachcd. but thc outptltwill contain Icss high frcqucncy noisc. To achicvc tx>Ch. arapid responsc and a smooth OJltput. an adaptivc filtcr isFig.2 Multi-I.Al8d-Cells wei~ing ~emoncc again required. This noi~ fillcr must bc capablc ofadapting its time constant in resJX)Dsc to lhe ~ of thcsystem.Thc adaptivc fillcr is tlsed 10 eliminare lhe inhercnl noisewithin tl1e systcrn. 10 obtain a smooth output. to settle lhesampling rcsult and to havc a fast rcsponsc whcn thc masson lhe platform is changal. When the output is a function ofinput only. the numm of possible resJX)nscs is limited.bccause lhe output will settlc within a finite time after lheinput has ccased to varroA rccursive filler is simply a linear ditTercnce tXluationwitl\ constant cocfficients and nothing more: in practicc itrnay bc rcali~ by a SOOft program on a general purposedigital computer or by a special purpose integrated circuitchip. A familiar example of a recursive filtcr is lhe trapezoidmie for intcgration 19):Yn=Yn-l+O.5(un+Un-l)It is immediatcly obvious that a recursive filter can, as itwcre, rememtx:r ali lhe past data, since lhe Yn-1 value on lheright sidc of thc cquation cntcrs into thc computation of thcncw value Yn, and hence into thc computation of Yn+ 1.Yn+2, and 50 on. Othcr cxamples of a recursive digital filterare thc cxponcntial smoofhing forecast:Yn+l=aun+I+(I-a)yn (0<8<1)and thc doublc cxponcntial (this filtcr is cquivalent at twocxponcntial filtcrs from first order in serial. u'here thesccond filtcr dcals with the output of thc first) withadvantagcs in climination of thc high frcqucncy noise:a2xn+2{ l-a)Y"n-l-( l-a)2y"n-2Y"n(O<a<l)othcr cxample is thc trend indicator:Yn=a(Xn-xn-I)+(I-a)Yn-1 (0<3<1)Filters that use only past and current values of lhe dataare called causal, for if time is lhe indcpendent variable.thcy don't rcact to futule events but only paste ones (causes).Howcver by using rccursion (i.e., fecdback) to make lheoutput a fundjon of both output and input, the initialconditíon for lhe íntegration ís remembered throughout theentire estimation ofthc integrnl.9~1exp~nThenexty(n)=y(n-l)+ x(n)-y(n-l)Ny(n) - value to samplex(n) - value fram the nth conversiony(n-l) -Iast value samplooN - filtering constantFor N=I, x(n>=y(n), incrcasing N lhe influence ofthe lastreading in the valuc to sample is Icss. Thc filter response isfast for low values of N bot the output takes much time toscttle (smaJl oscillations). For high values of N the filterreSlXJnse is slowly until the output to settle.The SAPMAF bas an error integral. D(k):nLk=1x(k)-y(k-l)D(k) =Which represents lhe sum of lhe differences retwecn thc Thc microcontrollcr driven by a clock frequency oflast value samploo and lhe reading. TI.c error integral 16MHz (250 ns instrucljon cycle givcs a counting pcriod ofregulares: the numrer of readings to take into consideration 250 os). Thc conversion time <iMainOO was around 18 ms.and lhe filtering constant to choose. time which can re rOOuced by increasing lhe microcontrollerThe error integral is compared with a constant paramctcr clock frequency up to 25 MHz. The resolution attained ofdesignatoo ST ACK, which represents lhe ma.ximum 6O(XX) divisions can also re increascxJ.variation of lhe output refore to happen a transition. When T11e Fig.4 represcnts lhe response of lhe filtcr SAPMAF inthc conversions are randomly spread around thc result shown thrcc difTcrent cases, whcre N, thc filtering constant is(rcading), D(k), thc integral of error fIuctuates around zero. respedively 2, 8, 32 ( the figure was ~ainOO with Simulink.When there is a change on lhe wcight. D(k) gross steadily tool of lhe Matlab version 4.1 for Windows) lhe normalizedand afier fcw convcrsions cxccx:ds the threshold levei frequency was ~ainOO with middlc sampling f~uenC'!r'ST ACK. equals a ORe. For N=2 the respon~ achievcd was rapid andThe error integral is restarted to zero when its valuc thc curve in lhe domain of tltc frequency is smooth. T11Ccxccx:ds lhe STACK. Thc fillcrs algorithm is: responsc for N=32 was a curve very abrupt and lhe change ofthc inputs is very slow in thc OOtp1t.toput..: y(O), ST ACK, x(k), N.Outputs: y(n).Variablcs initiation: y(O), STACK, N .Y(n-1)nIF' [D(k) = Lk=1x(k)-y(k-I) ] > ST ACK mEN(I)IN=N). )ow filtcring constanl) wilh N)=I. 2.4. .,.Ishort numbcr or readings)Ircstarting D(k). wilh D{k+I)=O)x(n)-Y(n-l)110 samplc y(n)=y(n-l)+ N ]ELSE[N=N2, high filtering constant J with N2=8, 16,32, ...Jlarge numbcr of readings)_I_' __1- .,x(n)-y(n-l)(to sample y(n)=y(n-l)+ N 1By choosing N as 2n. the filtering algorithm can be madesimplcr as faster and thc division in equation (I) can beimp1cmenloo by simple shifts.111. EXPERIMENTAL RESUL TS~~L.0 O, 02 03 0.4 05 ~8 ~7 ~. ~. ,~~Fig.4 Behavi« ofdle SAPMAF f« thec values ofNThe Fig.S shows ~ SAPMAF output versos ~ inplt.The output curve tnes to follow tlte input curve (the graphicwas built with thc T ABLE I rcsults).0uIpu1npu1~soINPUTFi8-5 Behavior ofthe SAPMAF ou1pd _8 inputThe table 1 shows for a singlc system of ORe smart loadceU the digital output when a mass is on the mini-platform.The STACK value is 15 (maximum osciUalion aroundavcrage) and filtering constanls are for rapid lransilionsN=I, for smoolh transilions N=4. Whcn a sample Xnchanges fast, lhe output's systcm neccls only two convcrsionsto OOCain lhe right ORe.According wilh the integral error, D(k), il's ~DIcchoose diffcrent filtering constants, allowing high values ofN for small oscillalion ofD(k) (examples: D(k)=2 and N=32,D(k}=6 and N=16, D(k)=IO N=8, D(k)=12 and N=4).TABLEIDIGITAL OUTPurs FOR ONE LOAD CELLIV. SOF1W ARE CALmRA TIONThc calibration process rncans the calculation of thcmultiplying cocfficicnts. which are given by lhe solution of asct of equations. opcration easily perfonned b}' any generalpUIJX)SC micl'(K:OmlXltcr.To tcst the software calibration mcthod for the multi-loadreli weighbridgcs. it was decided to usc standard rcadoutunias instead of the proto~ 3tX)\'c refcrred. A localwcighting (XJuiprncnt manufacturer madc available m-o 4load cells platfonns and R digital rcadout unias withnctwooong facilities. L<*I cclls taking a maximum nominalwcight of IOOkg, with 3000div ~Iution and a sensitivityaround 2mVN. were used. Thc 4-load relI platfonns coupledto a singlc readout unia is rated to 200kg with a resolution ofIOOgr. Each of the reaOOut unias were calibrated to givcaround 6Okg with a 2()gr ~Iution.Two seIs of tesas wcre done. one for a 4 load cellsplatfonn. and another for a 8 load cclls systern. using two 4load cells platfonns.The calibration mcthod consists on doing N readings ofwcight on cach load ccll obtained by moving a mass with aknown wcight around the platfonn. Thc number of readingsis thc samc as lhe number of load cells under the platfonn.Thc best results are given by lhe N readings ootained.concentrating the wcight as much as JX}SSible above cach oncof lhe N load cells.For thc 4 load cclls platfonn 4 seIs of 4 readings weremade. and the wcights found were uscd to workout lhemultiplying aK:fficients. ~ fadors affecting each one ofthc rcadings. cnablcs the corrcct cvaluation of thc wcightabovc the platfonn A sysaem of 4 (XJuations and 4 unknownswas built:KIWII + K2WI2 +K3W13 +K4WI4 = WKIW21 + K2W22 +K3W23 +K4W24 = WKIW31 + K2W32 +K3W33 +K4W34 = W~93KIW41 + K2W42 +K3W43 +K4W44 = WThe solution of this system gives the K factors rcquired tocvaluate the weight of an unknown mass (with thc Wrcreadings calculated with a calibrated mass positioned in fourdifferent placcs). With lhe following Wrc readings calculatedwith a calibratcd rnass of 20kg positioncd in fotlr difTcrcnlplaces:- Wlc rcadings 3.86, 9.96, 6.82, 0.72;- W2c readings 1.74,2.94, 10.88,5.58;- W3c readings 4.50, 0.74, 3.54, 13.26;- W4c readings 13.30,2.92, 1.48,4.38;the Kc factors evaluatoo:KI = 0.90025, K2 D 0.91580, KJ = 0.99196,K4 = 0.88685.After caJibration lhe resolution of lhe next equationdetennines lhe weight of lhe mass on lhe platfonn (withWi's lhe output's ofeach onc Smart load Cell).Mus - K1*W1+ K2*W2+ K3*W3+ K4*W4Using lhe K's factors severaI (25) weighting operationswere dane, with different masses, kx:ated in different pointsof lhe platfonn, having ~n recordtxl very encouragingresults, with errors bclow 5Ogr (4(xx) divisions in 2(X)kg).For lhe composite platfonn with 8 load cells tlle tcst wasrepeated and lhe 8 multiplyjng coefficients were calculated.The weighting tests dane confirmed lhe approach followedgiving errors below IOOgr, i.e. again 4000 divisions in400kg.V. CONCLUSIONS ANO FUTURE WORKThe use of digital signal p~ng I~hnjqucs can greatlyimprove lhe fl'(Xluency I'CSpoDK of aransdua:rs. TheSAPMAF' had shown 10 ~ able 10 give Ihc nxtUi~perfonnancx: (thc compensaloo outpul has a rasa reSfX)DKtime although lhe steady-slate condition). lllc rcsultsachievoo so far were very cncouraging rcgarding stalic orquasi-slalic weighting applications. Now we are alrcadyworking on thc new filtering algorithms having in minddynamic weighting. In dynamic weighling systemsconvcntional filtering melh<xJs cmployoo have limitalion inimproving accuracy and Ihroughpul ralc. Thc Kalman filtcrmay providc efToctive alternalive 10 Ihc convcnlional mclh<xJes~ia))y when the systcm is non-linear and low f~uencynoise is incorporaloo in lhe bandwidlh of lhe useful signal.We are also Irying a new calibration system based on avery simple backpropagation neural nelwork with as manyinpuls neurons as lhe numbcr of load cclls (four in our case),tive hidden neurons and lhe OUlpul neurons dcfined by Ihcnumbcr of bils I'CQlIired by Ihc rangc for Ihc \\'cighling «() toI(KK)kg l'CQuires alleasl 10 neurons) II()I.ACKNOWLEDGMENTSWe wish to thank CEL-Cachapuz Electrónica Lda. aelectronic weighting (XJuipment manufacturcr. for promptlymaking available to us lhe platfonns and lhe rcadout unitsuscd in tllis \mrk.VI. REFERENCES0.99196. [I) C. Couto. J. Higino. "Intelligent Signal Processing ForRatiornctric Data Acquisition: A Lo\v CosI SolutionFor Load Cells". Proceedings of IMEKO TC-4.B~Is, Belgium - Mar 1994.KJ(2) J.E. Brigncll, "Adaptive fillers in load cell reSJX>nscco~ion", Proce(XJings or Eurosensors VI-1992.University Southampton.131 "A high ~rfonnancc industrial wcighting systcrn".Application lK>te 295. National Scrniconductor.C. Couto, J. Higioo. "Smart Load Cells basOO 00Switclm-capacitors ". ~iogs or ICSPAT'94.Dallas. USA - Octobcr-1994.(4)(5] W. J. Shi. "Dynamic fnxtuency comJx:nsation fortransducers". Ph.D. Thesis. Universit), ofSouthamplon. 1992.(6] J.H. Huijsing. "Inlegraloo Smart Sensors". Sensors andActualors. Vai AJO. ppI67-174. 1992.(7) "A 20 - bit (I pprn) Linear Slope Integrating A/DConvcner". Application Note 2()(). NationalSerniconductor[8) J.M.Riviére. J.M. Robcrt. F. Hcrmann. C. Aubum.uM<xIclling of smart sensors: Application to a smarttempcrature transmitter". Francc.19) R. W. Hammíng, "Dígital filters". Prentíce-HallIntematíonal Edílíons. USA - 1989.IIOJ A. Tavares. J. Higino, C. Couro .Calibmlion or aMulti-Lood Cells Weighting Sysiem Based on NeuraINetworks", acccpled 10 ICSPAT'95, Boston. USA.<kt~-1995.Cj~y

Digital filtering in smart load cells

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Digital filtering in smart load cells

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