Adaptive Multilayer Neural Network Control of Blood Pressure

Transcription

1 Proeedng of st Internatonal Symposum on Instrument Sene and Tenology. ISIST 99. P (ord format fle: ISIST99.do) Adaptve Multlayer eural etwork ontrol of Blood Pressure Fe Juntao, Zang bo Department of Eletron Engneerng, Unversty of Sene and Tenology of na Hefe,Anu 36, P.R.of na E-mal: Abstrat: In s paper we dsuss a two model multlayer neural network ontroller for adaptve ontrol of blood pressure usng sodum ntroprussde. A model w auto-regressve movng average, represent e dynams of e system and a modfed bakpropagaton tranng algorm are used to desgn e ontrol system to meet spefed obetves of desgn and lnal onstrants. ontroller smulaton sows at t as aeptable settlng tme, t an be able to mantan e blood pressure wn e desred set pont. w a small varaton n e steady state. Keywords: Multlayer eural etwork, Adaptve ontrol.introduton Te mantenane of dereased level of arteral blood pressure(abp) s of vtal mportant n many lnal stuatons. ontnuous nfuson of drug su as sodum ntroprussde(sp) rapdly derease e blood pressure. Manual adustment of e nfuson rate of SP to ontrol ABP s often omplated by e varaton of patent response to s pressure-ontrollng medaton and by operaton by nexperened personnel. Lak of tmely adustment of nfuson may yeld undesrable osllaton. Reognzng e need for automat drug admnstraton system to mprove patent are, several losed-loop feedbak systems to automatally ontrol rates of nfuson ave attrated nterest. Intally, nonadaptve meods su PD and PID ontroller were used to regulate arteral pressure about a set onentraton. Tese ontrollers were not able to aeve satsfatory performane n e overall system beause of e nonlnear nature of patent response and dverse patent senstvtes to e drug.. More omplex adaptve and optmal ontroller need system parameters a pror. eural networks appear to be powerful tools to learn stat and dynam nonlnear system. Several ontrollers based on multlayer neural network(m) w an algorm for bakpropagaton of error serve to approxmate e unknown nonlnear stat funton. So multlayer neural network ontrol maybe powerful to ontrol rate of drug nfuson..plant aratersts A ontnuous-tme determnst model of e

2 ABP of a patent under e nfluene of SP s represented n equatons publsed by Slate: P( Po + P( + Pd ( + v( T s Ts P( Ke ( + ae ) () u( ( + T ) s were P s e ABP, P s e ntal blood pressure and P s e varaton of pressure due to nfuson of SP, ν s stoast bakground araterst. Te ARMA model of a patent s ABP under e nfluene of SP s y( P( K) P q d ( bo + b a q o m mq ) ( q) u( + a q w( () were y ( s e atual drop of blood pressure. w ( s a broadband random sequene and (q) s stable polynomal. 3.Desgn of ontrol system An overall semat of ontroller s sown n Fg. P s e ommanded pressure setpont. A two-model ree-layered neural network based on e bakpropagaton algorm s used to onstrut a nonlnear ontroller to trak e desred output. Te wegtng-determnant unt(du) s used to determne and to update e output wegtng fators of e two parallel M ontroller. For maxmzng patent safety, a nonlnear unt was bult nto e system at lmts e atual nfuson rate, u ( u, f u um u (3) um, f u um 3..Desgn of eural etwork ontroller A neural network based ontroller s desrbed as follows. A neural network w nonlnear elements offers dstnt advantages over onventonal lnear adaptve ontroller to aeve e desred performane. Due to e slow learnng speed of e neural network, t s neessary to ave speal onsderaton wen employed n adaptve ontrol for large dynam range of parameter gans and tme-varyng plant. It s preferable to arrange two parallel M ontrollers to meet e lnal requrements of automated SP nfuson system. One M ontroller s to map e learned range of large-gan and e oer s for range of small gan, funton of e system aratersts. Te ree-layered M arteture for system w SISO,sown n Fg.,s defned aordng to e bas nonlnear proessng elements. Te nputs of s M ontroller, e system s desred drop of blood pressure y ( O X,are P P ) and trakng error ( e y y) at are all normalzed by y.aordng to (),as e system output dereases as e ontrol nput nreases, t s a negatvely responded system. Beause of nterpatent and ntrapatent varatons n e response of e subet, t s neessary to adapt e wegts of M ontroller onlne to trak ese varatons.so e omputaton of ea M ontroller for SISO system u l s sown as follows: ) Te output of e HIDDE layer: H (

3 P P O M M U U Um Um U Plant P DU Fg. ontrol System Dagram e Delay X U l Delay ( ) X OUTPUT Layer X - X IPUT Layer HIDDE Layer Fg.Tree-layered ontroller(m) H O ( O ( k ) ( k )) + e X (4) U l + e Q( ( Q( k ) φ ( k )) H (5) ).Te output layer U l ( 3).Te wegts are updated from HIDDE to e OUTPUT layer; ( k + )

4 ( k + ) + n δ H (6) δ [ y ] u l [ u l ] 4).Te wegts are updated form e IPUT to e HIDDE layered: ( k + ) ( k + ) H + n δ X (7) δ δ [ H ] 5).Te bas are updated at e OUTPUT and HIDDE layer: φ ( k + ), ( k + ) samplng tme parameter ψ k k k k,e DU alulates e ψ ( ( P( k ) P ) /(( P P ) k ) (9) Tus,, an be determned aordng to e parameter ψ and deson rule. Rule.If ψ <..8, Rule.If ψ. and ψ <. 5.9, o φ( k + ) φ( + n δ φ ( k + ) n δ were (8) Rule 3. If ψ. 5 and ψ <.., Rule 4. If ψ. and ψ <. 5 η > s gan fator of bas at OUTPUT layer Φ η > s gan fator of bas at HIDDE layer 3.Desgn of egtng-determnant Unt e selet e same four nputs at e IPUT layer of ea two model M ontroller, w are all e normalzed values of desred blood pressure drop and trakng error: Te number of HIDDE nodes s also osen to be four aordng to entensve omputer smulaton. In order to determne ea ntal output wegtng fator of e parallel two-model M ontroller, frst, only e frst M ontroller (, ) s lose to exte e system for k samplng perods. Durng s perod, e DU summed e measured ABP. At e., Rule 5. If ψ. 5 and ψ <..9,. Rule 6. If ψ. and ψ <..8,. Rule 7. If ψ. and ψ <. 4.7,.3 Rule 8. If ψ. 4 and ψ <. 6.6,.4 Rule 9. If ψ. 6 and ψ <. 8.4,.6 Rule. If ψ. 8 and ψ <. 9.,.8

5 Rule. If ψ. 9.en set.,. Te total output of e two-model M ontroller u an be omputed from e u ( u + u ) um () 4.onluson Smulaton results ndate satsfatory performane and robustness of e proposed ontrol n e presene of mu nose and unertantes and parameter varatons. Te applaton of two-model M ontroller to update e nonlnear and tme-varyng nature of e patent response resented n s paper llustrates at a ontroller of s desgn as e apaty to provde lnally aeptable regulaton of ABP usng SP drugs. ontroller smulaton sows at t as aeptable settlng tme, t an be able to mantan e blood pressure wn e desred set pont. w a small varaton n e steady state. Te smulaton ndate at a ontrol system of s desgn as mprove performane ompared to oer knds ontroller n ts robust performane, smple arteture and algorm and no requrement of system parameters dentfaton a pror. ow we need to test e system by experment to verfy ts pratal applaton. of blood pressure. IEEE Trans.Bomed.Eng.vol 3.pp68-75, R.E.ordgren, P.H.Mekl. An analytal omparson of a neural network and a model-based adaptve ontroller. IEEE Trans. eural etworks.vol 4.pp , M.J.lls, G.A.Montague. Artfal neural network n proess estmaton and ontrol. Automata. vol 8.pp8-87, J.F.Martn, A.M.Sneder. Improved safety and effay n adaptve ontrol of arteral blood pressure roug e use of a supervsor. IEEE Trans.Bomed.Eng.vol 39.pp38-388,99 Referene. J.B.Slate and L. Seppard. Automat ontrol of blood pressure by drug nfuson. Pro.Inst.Ele.Eng. vol 9,pp ,98.. H.J.zek, R..Jllffe. Speal ssue on adaptve ontrol and drug delvery. IEEE Trans. Bomed.Eng. vol 34,pp , J.M.Arnsparger, B..Mnns.Adaptveontrol