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A Prototype Expert System Based on Laguerre Adaptive Control
Copyright © IFAC Intelligent Tuning and Adaptive Control. Singapore 1991
KNOWLEDGE BASED CONTROL! ADAPTIVE CONTROL
A PROTOTYPE EXPERT SYSTEM BASED ON LAGUERRE ADAPTIVE CONTROL Wei-Wu Zhou·, G. Dumont U and B. Allison. 'Pulp and Paper Research Institute of Canada. 3800 Westbrook Mall. Vancouver. BC. V6S 2L9 "Pulp and Paper Centre. Dept. of Electrical Engineering. University of British Columbia. Vancouver. BC. V6T IW5
Ahst.l'act. !'al'l. 0[' itlt experl. cont.rol (Ee) project. whicll IIS( 'S Lil!2,II<'I'I'( ' 1111 ,.,11'11,· tltrcu self-tuncr (LUST) t.cchnique is presented. A cortllllercial exp"rl sySI('111 slll'lI
G2
has been useu to uevelop the expert knowledge for cOllllllissioning and IIIOIIII(jI'-
illg thc adapt.ive controller. The present prototype is developed to CO llllllis si() l llll~ allll Illollil.ol'ing 1.111' LlIST itdapl.ivc cont.l'olln. Sillllliali()ll.s ()II l'I' (l('('ss ('<1\111',,1 II( piliI' Illill giv(: s;lI.is['acl.oI'Y l'.,slIll.s. Keywords. Adaptive control, Artificial intelligence, Expert. ';,I'S\.I' III. I~xpcrl ini<'lIlg('111 control, Process control
In INTRODUCTION i'vlodel'n adaptive con 1.1'01 techniques provide the citpabi I iLy to all tOlllatically adj w;1. con trol acLions and t.hus ensure optimal performallce as process operating conditions change.
Although much re-
search wo rk on adaptive cont.rol has taken place (jVI'1' 1.11<' l'il SI. I.wo d.,cadl:s allll SOIIIC SIICCCSS['1I1 <11'I'licall"lls Ilave 1)(:1:11 dl'v, :lopcd , il. is, IlowI:vI :I', st.ill nol. extetlstvely useu in industry. Onc main obstacle towaru widespread applications is a shortage of people with the spccialized knowledge required for cO llllllissi o ning such systems. Such knowlcdgc is 1'(''1llil'( '" ['()I' c hoosillg il.ll\(!Ilg 1.11<' 1IIIIIInOllS I:x ist.Illg conl l'ol ,.;cllelncs, I.llc itlgoril.hlll I)cst sllil.ed to t.he problelll at hand.
Expert. systcm technology
Ilas shown t.he pot.ential 1.0 proviue t.he expertise Ileces,;ary to hclp it non-expert to commission and Illollil.ol' illl adaptive cont.roller.
learning control Sysl.ClIls, the co11I 1'0111'1' i,.; ."; 1'1'-
posed to bc able to est imatc IInkIIOl\' 1I illfol'III;11 il ' ll during its operation and uctel'lnillC all op!.i lll ;d (,01 1trol action from the est imated inforlllal.ion. \-:Xillllpies of work in t.hc arl'it iU'I' 1"11 (I!)i I) illld S;ll'idi~
(1977,1981). Expert Sysl.clllS Ilscd ;tS "II'-li l\('
I""I~
during process and con twl designs. ill COil till Ililtion with a control system computer-aided d" .,.;igll. a.re airnr:d t.o C.itpl. ll\'(' kll()\\'I(,t!g;" ;1j,"111 1111\\' I ll>' pro('I 'ss sholll" I,.. "1'.~lglII''' I11 111'.11'\' I" I'IIIliI \: 111 OilS ol)jecl.ivcs (cull!.I',,1 '11"dlll( "~' (·(·() II"III .' "1,') . ,,1101 to capture knowlcuge al)(>IIt pruc(~ss (,()llli~III · "II " I(. control specificat.ions and diff'erellt cOII!.l'ol ""slgll techniques (e.g. SISO cornpensation, 11111111\';11'1able design dc). Examples of work In t.l1(' ;11"';1 i\I'(' 1'01111<1 ill Fisher (1!J8(j); 'j'aylor (1!)X(j): \iida ;111.1 Umeda (1986); MacFarlcule cl Ill.
(I!Jt'i): 1,"\\1 11
and Morari (1988); Birky cl Ill. (IDBX)
Olh"1
(X-
arnple on plant-wide control sl.rillq~y pl ;lllnill .~ '" found in Stephanopou los cl al. (I DKi) ;\ 1110)1'( ' well-known expert systClI'1 applic;tlioll I11 I ""'·'· ... ,., control is to use all expert SYSt.CIII ;\.,.; ;1 1'111111,1(·-
An expert system can loosely be described as a computcr program that uses stored knowledge to r:l lllliatr: the problern solving behaviour of human experJ. ill some limitcd domain. A process control I'XP':1'1. Sysl.clIl is a lIal.llral ext.ensioll of itd-
ment to a conventional control SystCIII /'01' l'r()u'SS monitoring and alarm analysis. TIll' (" X[1<:1'1 systcm is rnainly used as all operator ilssiSl.illll. \\' 11\1·11 packages t.he kllowledgc 0[' cXI,criellc,~d ()llI'lilIIJI·'"
The development of
NIIIlICI'OIlS cXillllplcs of' difkl'l ' ll!. 111()IIII(l\'Ill~ ill'loIl catiolls can be /'oulld. i\ ['CIY eXiu llples <1\'(' \,,1-
slIch Cl system has been an important rcsearch area
son (1982) on nuclear power plan!.s , Sakaglll'lll illl"
in hot.h expe rt system and proccss control since 1111' "ilrly I ~)K()s. Expl'l'J. sysl.crns in process conl.rol
Matsumoto (1983) on electrical power SYSt.CIlI;< il llt! Palowit.ch nncl I\l'illlll'r (InK:») OIl rlll'lllir;t\ 1'1;11 h.
v,lIlccd control technology.
hill'I' I)( '(~ II inil.i;t!ly /'ocllsI'd Oil Ical'lling and s(~I/,
H.ccc llt.ly, it.iOllg with I.IIt' ilx;IIIi1I)illl.y
orgilllizing cont rol.
cial real-timc expert SystCl1I shells, 1IIIICll ill io'llll()11
319
,>I' C()IIIIIII'I'
has been paid to developing expert system to realtime process control, which is viewcd a.s expert control (EC) or knowledge-based control (KBC). In th e area the expert system is typically used on a small part of the plant (c.g., singlc closed loop) alld pcrforllwd <1.<; a slIpcrvisor to COllllllissioll and tune controllers, monitor pro cess, pcrforlll fault diagnosis, and restructure control systems, etc. Examples of such work can be found in Moore et al. (1984); Astrom et al. (1986); Arzen(1987); Karsai et al. (1987); Liu and Gertle (1987); Zhu and Dai (1988); Tzollana.s cl al. (1988); Whitlow and Debelak (1988) ; Basila and Cinar (1988). The work of the present paper belongs in this category. The goal in the long-run for expert control is to build an "intelligent" controller, which has increme nt a l learning capability and can be viewed as expert intelligcnt control (EIC). In this paper a prototype expert system using a Laguerre adaptive control technique, which IS a part of a long-term EC project, is presented. STRUCTURE OF EXPERT CONTROL Expert control is a very advanced and comprehensive branch of modern control technology. It , in fact, deals with a rathcr wide range of disciplines and technologies in both control theory and artificial intelligence (AI). In aspect of control theory, EC may consist of all basic control algorithms, e.g., PID control, auto-tuning, identificat.ion algorithms, adaptive cont.rol algorithms , dc., which constitute a toolbox of lIl11ncrical algorithms for different control strategies. The numerical algorithms provide explicit optimal solutions under some certain conditions. In aspect of artificial intelligence, EC may consist of knowledge base, knowledge representation, inference engine, data base, etc., which constitute a expert "brain" for emulating "intelligent" problem solving behaviours of human expert. In this part, the knowledge is presented symbolically and the problems presented are IIsua.lly fu:",,:y, cOll1plcx and difficlllt nlllllcrically to be solved; instead heuristic deduction and reasoning processes are used. EC is thus a combination of modern control technologies and artificial intelligence, and is in the frontie rs of modern science and tech no]ogy. The st.ruct IIrC of EC may ])(' shown as in Fig . 1. Control of a process basically involves two types of knowledge : control knowledge and proc ess knowkdgc. Control knowledge contains knowledge of alltomat.ic control, C • .rJ., knowlcdge about. ident.ification and different control strategies, etc. Process knowledge contains knowledge of the process that should be controlled, e.g., knowledge about
norma] and abnorlllal cO llditiollS , st.;II.ic ;II,d dy namic characteristics o f t.11I: proc",",s , cl c TI,i,; knowledge can be acquired through ol l-lill<' I'xl)('riments or from expericnced process ellgillf'l'I"s . III Fig . 1, the blocks of 1I111lf( ~ ric;r1 ;rl)!;orit 11111'; "lid <">1 1I.rollcrs cOlIsl.it.III.<' t.hl ' cO Ilt.ro l kll()",I".j"ct,.; (related to facts o f the do main) and rules (r"i:rlcd to heuristic dedu ct ions and reasoning). TIlt' illference engine is the driving mechanism o f expe rl systems. It manipul ates rul es fr om the kn owblg,: base to [orlll illfercll ces alld draw (,O IH:I" .sioIlS . TII" database is used to hold alld rep resc llt ra.ct.s (1'1'01>lems) about the application domain. The implementation of an expert syslelIl for process co ntrol SystclIIS is not t.rivial wil.lr COli 11 11011 symbolical lallgllagc Sllclr as LisI' or "ru ing . TIll" separation betwee n the kn o wledge base and in !"ere nce engine has led to the development of so ca ll ed expert system shells or frameworks. An expert system shell is an em pty expe rt syst.e lll wil.lrolll rclinlillary 1'1,,;,,11.,.; from an EC project started in 1989 in the \-'all col lver Laboratory, Pulp and Paper Research Institut e of Canada. The initi a l lflotivat.ion was 1.0 IIIlild an expert. slIpcrvisor for COT11ll1issiollillg ;,,1<1 111"11itoring an adaptive ("o lll.roller. '1'111' l>rojl 'c l ("011sists of both hardware ,lnd software devel opII"'"I" as shown in Fig , 2 as a demonstration projecl..
320
A VAX Station 3100 running VMS and DEC Windows has been chosen as the hardware platform. The expert system shell G2 has been used for irnpl0.nwnt.ing t.h0. ~xp~rt . sysl.<~rn part. of t.he project. G2 is a real-timc expert syst.em shell with object-oriented knowledge representation. It provides schematics, dynamic models, and heuristics to represent the knowledge of applications . A standard int.erface GSI supplied along with G2 allows G2 to intcrface to external data sourccs including data acqllisition equipment, dal.iloascs, and ot.hcr extcrllal deviccs . The numerical algorit.hrns arc the main part of the software development in this project, alld consists of several packages of different algorithms about identification , adaptive control, 1'10 control, and managemcnt utility. Thcy are programmed in FORTRAN. A data communication bridge has been programmed in C to link the exp ert system G2 and the numerical algorithm packages. The G2 standard interface GSI provides specific functions for the communication purpose and is used to perform the development. A data acquisition equipment should be assembled to transfer data between the VAX-station and the actual controlled process . The expert system G2 and the numcrical algorithm packages can run on a single computer, as in our case, or on two separate computers. The measurements obtained from the process are transferred to the main computer VAX3100 and stored in data files. These data files constitute all exterllal database for the cxpert system and provide necessary information to both the expert system and the numerical algorithm packages. Such a system structure is aimed at matching the situations of hardware equipments commonly existing in the pulp and paper industries. LAGUERRE ADAPTIVE CONTROLLER Scveral adaptive control str a tegi es and id entification methods have been select.ed cHld used in the EC projcct in order t.o fulfil c1ifl"erent. reqlliremellts in a wider applications . COllsidering the rather wide range of different model structures and varying dead-times we have to deal with in industrial process cont.rol systems , the Laguerre unst.r1Ictured self-t.uner (LUST) mct.hod (Zcrvos and Dlllrlont, 1988) will first be implemented and tested. In the following a brief introduction of LUST is described .
process nOIse . An adaptive control scheme based on the ahove formlllat.ion uses t.he recllrsiv(~ le;\.<;t.-squar"s (Il.LS) met.hod t.o identify the parameter vect.or 1:. line, we use the exponential forgetting and resetting algorithm (EFRA) (Salgado et al., 1988), Theorems proving the global con vergellce alld stability of this scheme are present.ed in '/,crvos ;(11<1 Dlllllont (I D88). The choice of t.he pararnet.er l' in t.he L;(gllnr,' functions is not crucial. However, it inflltences the accuracy of the approximation of the plant. dYllalTlics as a truncated series. For Cl given plant , LilCrc exists an optimal p that minimizes the number or filters required to achieve a given accuracy. The chain of all-pass filters in the Laguerrc network provides good representation of a time delay T , ill particular when p 2N/T. The actual plant order hiL'; little bearing on the number of filters N. The horizon of the predictive control law is automatically adjusted on-line to assure closed-loop stability.
=
I3ased on theoretical analysis and on our illdustrial experience of the LUST algorithm, we knolV that LUST can perform an excellent control quality under the following conditions : • proper select. ion of the vallles for sampling interval T Laguerre time constant p number of Laguerre filters N • sufficient excitation for con vergent estimation of Laguerre spectrum gain vector c These factors should be fulfilled when a L liST con troller is used. IMPLEMENTATION
The limitations of the LUST algoirhtm chose n imply the following constraints on the process to be controlled , • sillgle-illPut and single-output. Sysl.<:1I1 • open-loop stable • well damped
The output of the plant yet) is described by, N
yet) =
L c;l;(t) + well = £6'l(t) + well
(I)
;=1
where
£6
[ll(t)
12(t)
[Cl
IN(t)
C2 ...
]T,
CN],
IT (t)
where the l;'s are the outputs from each Laguerre filt.er and wet) is the
The initial task is to commIssIon the LilgIWrr<' adaptive cOlltroller . For such a COllllllissioning, "n'eral characteristic factors of the process should initially be determined by testing the process or operator's input. In particular we require rough estimates of
321
Td 1~
Delay time H.cspollse or seUi IIg ti mc
the control law u(l) is shifted to be the ill(>llt. signal and the LUST controller is forlllillly cxccuft'd to control the process.
Because we then use difl'e rent rules whether the response is dominated by time delay or nol., we I.hen use the rollowing criterion ror discrilllinal.illg between the two cases: Time-delay No-delay
The procedure of commlsslonlllg is 1.11<'11 1'1'1'rormed in rules, r..9.,
Td/T> 0.25 Td/T < 0.25
where Td is the estimated dead-time and T is the dOlllillilllt I.illlc COlIst,lld, or I.he process and can bc calculatcd in a step rcsponse test that the time of the process response (without dead-time) reaches the point of 63% value of set-point, The operator is also asked whether the process is currclltly under a PlO cOlltrol 01' not. To determine above factors, a step response test is initially applied either in a close-loop (under an existing PID control) or in an open-loop (no PID control existed). Then delay time 'Id, response timc T, and time constant T are accordingly determined. Further the desired sampling interval T is calculated from the response time T, and the time delay T d . Thc number or Lagllerrc filters N alld constant time}J call then be calculated for thc followillg two cases (Dumont, Zervos ,~nd Pageau, 1990),
• Delay representation:
N
• IF process-status IS NO-PlO-EXIST TIII';i\ in order conclude that OPEN-LOOP-STEI'RESPONSE-TEST is TRUE; and set SETPOINT to INITIAL-SET-POINT; and inform the operator that "OPF:N LOOI' STF:I' H.F:SI'ONSE TEST is execut.illg". • IF process-status is YES-PID-EXIST TilE\, in order conclude that CLOSED-LOOPSTEP-RESPONSE-TEST is TRU 1::; and sct SET-POINT to INITIAL-Sf:T-I'OINT; i1l1d in fol'll I t.hc opcrator that "CLOSI':J) LOOI' STEP RESPONSE TEST is executillg". • IF process-status is NO-PID-EXIST; alld test-status is TEST-READY 'I'll EN ill Older set ADD-PRUS 1.0 I; alld set. ST,\H'I'LAGUERRE to 1; and inform the operator that "Initialization of Laguerre adapti ve controller, please wait." • IF proccss-st.atus is YI,;S-I'IJ)-I,;XIS'I': ;",d test-st.atus is TEST-REA IW 'I'll I':;-.J ill order set ADD-PRBS to 1; and set S'L\ InLAGUERRE to 1; and inform the ol'cr;ll<)I' that "Initialization of Laguel'l'e adclpt.i vc <:0 11troller, please wait." • IF estimate-status is GOOD 'I'll EN ill ()rder conclude that ADAPTIVE-CONTHOLREADY is TRUE; and set control-switch to (j; and inform the operator that " Laguerrc adaptive control is executing t"
T
> ~+1 T
• No delay: p= T
During the step response test (after obtaining proper values for Nand p), an identification experiment is performed for initialization of the LUST cont.roller. It is performed by sending a pseudo randOJll binary sigllal (P IU3S) tllat is addcd to the step signal. The Laguerre spectrum gain vector c is then estimated with open-loop signals. It means that the signals used as input signals in the estimation are I.hc comhined signals (st.ep signal plus PRBS), alld t.hat. the COlllPlltcd COli 1.1'01 laws n(l) from I.he LUST algorit.lllll arc, ill this case, 1101. yet used as the input signals to the process. After passing a period to allow near convergence of the estimator,
322
As mentioned previously the nUIllcrical illgorithm packages (written in FORTRAN) are IUI1ning parallel with the expert system G2, s01l1e control flags using digital number 0, 1, ... illT t.iI'ls 'I""d to pass COrrllll'
SIMULATION An example for commissioning and monitorillg the LUST controller used on a bleach-plant extraction stage in pulp mill has been si mula t.cd. ,\ process schemat.ic of the bleach-plilllt cxtr;I'" i011 st.age is developed 011 C 2 alld sho\\' 11 ill I: le-; :\ The objcct.s developcd for t.iw ap 1'1 ic ;If .ioll I1 ;1\1' physical instances which arc illu::;Lrut.cd ()II '1\1' schematic, and are knowledge represented by those
related attributes a nd rul es of G2, External se nso r s whi ch a r e r el ated to th e obj ec ts are develo ped Oil (;2 in o rd er 1.0 transfer data or rn es,~,,).!, "" 1)(' 1W"" II I II<' c'x t.,' rll;d pl' (),','~s illld th, ' ( ~ X1" '1' 1 "y" t.-III. 1'''1 1' si llllti;1I i0 11 pllrp u,s,',S , 1.11<' 1'1'0c,',"" is "lllll ia l, 'd I);.' it th co l'<'I i cal IlIo d el w hi c h is it fi r sl o r d er IlI o d el with tilll e delay, Th e procedlIrc o f CO IJ l llli ss io lling h as I )( ~ d esig ned to b e ab le 10 1'1111 Oil I.wo cases: :.IO-I'ID-EX I ST a.nd YES(,ID - EXIST , whi ch arc rc l at.cd t o OI'E N- LOOPS TEI' - I{ESI' O:.lS I~ - TEST alld C LOSED- LOOI'STLI' - IU:S I ' O.\SI ,:- TEST r('s p ec l ively, Th e CO II11'01 I
CONC L CS IO\S Tllis Pl'ot()t.yp" h ilS sll(I\\'11 P:'I'II ;d r" , ,,tih " I' :11, 1':<': proj ec t.. Ollly ti ll' cO IIIIIII""i u llil' ).!, par i " I' il L UST co ntrol using t il e ex p ert. sys t. c l ll l, :c hlJ iqll",,, has bee n intro du ce d in this p ape r , \I o nil o rin g fun ct io ns arc cllrren t ly bei ng d evei o pc 'd ,
M' I( .\ () \ \ ' 1.1,: 1)( ; 1-:\ 11,: :\ 'IS Th e finan cial supp ort o f lit e Scic lI (, " ( 'o'lll cil (, I' 13ritish Co lumbi a thr o ug h ST I) I.' (;r illll #~"- ':.'1.1 I grat.dull Y;L(, kn o wl ed ged,
Set po int PlO contro l ,~dJPli\' e control
way to t.ran sfer d ata b etwee n C a nd FORTRAN in th e VAX YMS en vi r o nment is through m a ilb ox, Th e kn ow l edge o f comm issi oning th e LUST co ntro ll er has b ee n rep r ese nted in both heur istics a lId in FOHTRAN, With cl ear and cert.ain obj ecti\'e i n mind , a n overall consi de r ati o n fo r opt ima l prog r arlIlIl ing to fulfil th e targe t, is n ecessary a nd sig llifi cant , It means that if th e kn owledge can be easily represented in a numerical way, then it may not be necessary 10 IJ.~" Il cl lri st.i('s, Af't' ~ r d eve lo plIlcllL o f' I he knowledge r ep rcse lIt. a t.i on fo r th e sp ec ifi c p r ocess, \\'hic h co n tailIs the d eve lopme nts o f obj ects and nIl !'s, th e expe rt. sy st. em G2 is t hc n r eady t o ('o nl r o l 1111' pr ()c!'ss with its fill ed ex perti sc, 1'1'0cc- dures for I Il c cO lIlllli ss io nill g ;lIld Ill o lliLo rin g tllC I TS T co nI r o ller have bee ll d( ~ s igll c d \ViLh g uicl ed st eps,
Expert System
Fig, I, Stru ct ur e o f Ex pc rt. CO lltl'o l
- -- - - _ ' _ _ ,: C'0ntrc.llel's , ,i - - , , 7,'<:'" ,. - - - - "
I'
I.
c, t o a ll sw er th e Il ecessary qu es tions
\\'lli ('h o pr'l'a l or Ci ln rHo\'ici c (e,g , th e p r ocess IS CIIJ'J" 'l ll ly 1IIId,' 1' a 1' 1i) CO IIII'(l l" ), Afkl' illlSW Cl'Illg Ill<'sc '1 II c,, 1lUll" lll c expe rt SySl<: 111 ca ll auto lll at ica lly pe r fo rm it co rr ec t. proce dure fo r co rnmisslo nlllg and t o m Ollitor t he per fo rman ces o f th e kn o wl eclge, Sirnulations co ntro l \\'Ith its exp ert 0 11 I Iw ilppli c;11 iOIl havc ShO\\' 11 a I' ilt. her co nvin cing r"~ltits () II "XP'; I't. sllp c r v iso l'Y to t.h e CO lJllllissi o llillg;
pr oce dlll'c and Illo nito rin g capability.
PROCESS
I: i
----~~--~,
Data
Acqu lsltt cn
i
,
i.'_ ' :..,- --
: t
"
sz
eri~31
'i urr..
.
(
lg_tr_i_tL_II:_ 8_ ' _ _ A_
"
KnOS"'~dge I ,~
~~
Data " '",,_ _-::B~a,ge_ _--,pA,1 _ _E-,.3""se_ __
..;'::> !
j
"", 7
Inferen ce En/i:ine
Cse:r
lnlerfuce
Fig, 2, SystClI1 Stl'lI ctlll'r
323
r\
j : 1 '1
,~-----~I
T lIe o pe r ato r is gUided thro ug h t he initial
proced ur es ,
I
i\.,j
'1
-
6,
[o'u, K .S. (1971). Learllillg cOlIl.rol sYSL('111 alld intelligent control system - ... All illtrodllcl.ioll of artificial intelligent and automatic control. IEEE '!'rans" AC- 16, 70 - 72 . .Jackson, P. (198G). [lIl.rocillcl.ion 1.0 I':xpnl. Systems. Addison-Wesley: Readillg , MA. Karsai G ., E . Blokland, C. Biegl, .J . Sztipanovits, K. Kawamura, N. Miyasaka and M. [lIl1i (1987). Intelligellt supervisory cOlIl.roll<-!· for gas distriblltioll sysl.(~1\1. I'ro(' . I~)KI All\, Con trol Conf., 1353-1358. Lewin, D .R . and M. Morari (1988). ROBEX: an expert system for robust controller synthesis. Comput. Chem. Eng., l2., 1187-1198. ,\ slIperLiu, K . and J. Gertler (1987). visory (expert) adaptive control scheme. IFAC 10th World Congress. Munich. MacFarlane A.G.L. , G. Gruebel and .J. ,\ckcrmann (1987). Future design cnviWllllWllts for control engineering. IFAC 10th World Congress. Munich.
Fig. 3. Bleach-Plant Extraction Stage
REFERENCES Astrom, K.J ., J.J .Anton and K.E.Arzen (1986). Expert control. Automatica. 22 , 277-286. Arzen , ICE. (1987). Realization of expert system based feedback contro!' Ph.D. thesis, Dept. of Automatic Control, Lund Institute of Technology, Sweden. Basila, M.R. and A. Cinar (1988). MOBECS: Model-object based expert control system. AJCHE Annual Meeting. Washington, D .C . I3irky , G.J ., T.J. McAvoy and M. Modarres (1988). An Expert System for Distillation Control Design. Comput. Chem. Eng ., l2.(9/10). Carmon, A. (1986) . Intelligent knowledgebased system for adaptive PID controller . Journal A . 21, 133-138. Cameron, LT. (1986). Expert systems for hazard and operability studies of process plants. l'roc . Allstraliall IlIstit.lIte of Pdroleuln. Melbourne, pp. 1-12. Dumont , G., C.C. Zervos and G.L. Pageau (1990). Lal!;lIcrrc- hil.~ ( ~ d adapl.i v(' COli 1.1'01 of ill ;111 illdllsLri;li Id( "lcit plalll. ( ~x l.racLioll sl.;tI , ( ~ . AIILolllaLiGt. :2.').
p"
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324
Moore, R.L., L.B. Hawkinson, C.G. Kllickerbocker and L.M. Churchmall (108,1). ,\ real-time expert system for process control. Proc. First Conf. on AI Applications. IEEE Computer Society, 529-576. Moore R.L. and M.A. Kramer (1986). I;: xpert system in on-lille process cOlIl.rol. III M. Morari & T.J. McAvoy, Eds., Chelllical Process Control - CPC Ill. Elsevier, Amsterdam, pp. 839. Nelson, W.ll. (1982). IU::ACTOlL all experL system for diagnosis and treatment of nuclear reactor accidents. Proc. of the National COliC on Artificial Intelligence . Pittsburgh, PA , pp. 296-301. Niida, K. and T. Umeda (1986). Process COIItrol system synthesis by an expert system. In M. Morari & T.J. McAvoy, Eds., Chemical Process Control- CPC Ill. Elsevier, AlI1sU~r dam. Sakaguchi , T. and K. Matsumoto (1983). De velopment of a knowledge-based expert system for power system restoration. IEF:E TrilllS Oil
Pow(~r Apl .~;.:ral.lI~.'.:'.~ _S'y"I~> JJ):!. :!
Salgado, M.l·:., e.;.C. C;oodwill itlld H.II. \1101,11, ·· ton (1988). Modified least squares ;Ilgolilhlll incorporating exponential resettillg illlrl forgetting . Int . .1. Control. 47(2) , 4n - nn, i
Saridis, G .N. (1077). Self-organizing cont.rol of stochastic system. Mal'cel, Dekker Inc. New York. Saridis, G.N. (1981). Application of pattcrn recogllit.ioll 11Iet.l!ods 1.0 cOlIl.rol sys1.e111. IEEE 'l'rans. , 26. Shinskey, F.G. (1986) . An expert system for the design of distillation columns. In M. Morari & T.J. McAvoy, Eds., Chemical Process Control - CPC (] I. Wilcy, New York. Stephallopoulos , G., J. Johnst.on and R. Lakshmanan (1987). An intelligent system for planning plant-wide cont.rol strategies. IFAC lOtl! Wo rld Congress 011 A IItolllal.ic Control. Mllllich. Taylor , J.ll. (1986). Expe rt system for cornputera ided co ntrol engin ee ring. In M . Mora ri & T.J. McAvoy, Eds., Chemical Process Control - CPC Ill. Elsevier, Amsterdam. pp. 807. Tzouanas, V.K., Georgakis, C., Luyben , W.L ., Ungar, L.H. (1988). Expert multivariable control. Comput. Chem. Eng. , 12(9/10). Umeda, T. and K. Niida (1986). Process control system synthesis by an cxpert syste m. Control Theory Ad v. Technol., 2.. Whitlow, J.E. and K.A. Debelak (1988). Expert system based process control. AICHE Annual Meeting , Washington, D .C. Zervos , C.C. and G.A. Dumont (1988). Deterministic adaptive control based on Laguerre series representation . Int . .J. Control. 48(6), 2:.l3:.l- 235D. Zhu , Z. a nd G. Dai (1988). Expert system approach of ind ustrial process adaptive control. Proc. IFA C Wo rkshop OIl Robust Adaptive Control. Newcastle.
325
KNOWLEDGE BASED CONTROL! ADAPTIVE CONTROL
A PROTOTYPE EXPERT SYSTEM BASED ON LAGUERRE ADAPTIVE CONTROL Wei-Wu Zhou·, G. Dumont U and B. Allison. 'Pulp and Paper Research Institute of Canada. 3800 Westbrook Mall. Vancouver. BC. V6S 2L9 "Pulp and Paper Centre. Dept. of Electrical Engineering. University of British Columbia. Vancouver. BC. V6T IW5
Ahst.l'act. !'al'l. 0[' itlt experl. cont.rol (Ee) project. whicll IIS( 'S Lil!2,II<'I'I'( ' 1111 ,.,11'11,· tltrcu self-tuncr (LUST) t.cchnique is presented. A cortllllercial exp"rl sySI('111 slll'lI
G2
has been useu to uevelop the expert knowledge for cOllllllissioning and IIIOIIII(jI'-
illg thc adapt.ive controller. The present prototype is developed to CO llllllis si() l llll~ allll Illollil.ol'ing 1.111' LlIST itdapl.ivc cont.l'olln. Sillllliali()ll.s ()II l'I' (l('('ss ('<1\111',,1 II( piliI' Illill giv(: s;lI.is['acl.oI'Y l'.,slIll.s. Keywords. Adaptive control, Artificial intelligence, Expert. ';,I'S\.I' III. I~xpcrl ini<'lIlg('111 control, Process control
In INTRODUCTION i'vlodel'n adaptive con 1.1'01 techniques provide the citpabi I iLy to all tOlllatically adj w;1. con trol acLions and t.hus ensure optimal performallce as process operating conditions change.
Although much re-
search wo rk on adaptive cont.rol has taken place (jVI'1' 1.11<' l'il SI. I.wo d.,cadl:s allll SOIIIC SIICCCSS['1I1 <11'I'licall"lls Ilave 1)(:1:11 dl'v, :lopcd , il. is, IlowI:vI :I', st.ill nol. extetlstvely useu in industry. Onc main obstacle towaru widespread applications is a shortage of people with the spccialized knowledge required for cO llllllissi o ning such systems. Such knowlcdgc is 1'(''1llil'( '" ['()I' c hoosillg il.ll\(!Ilg 1.11<' 1IIIIIInOllS I:x ist.Illg conl l'ol ,.;cllelncs, I.llc itlgoril.hlll I)cst sllil.ed to t.he problelll at hand.
Expert. systcm technology
Ilas shown t.he pot.ential 1.0 proviue t.he expertise Ileces,;ary to hclp it non-expert to commission and Illollil.ol' illl adaptive cont.roller.
learning control Sysl.ClIls, the co11I 1'0111'1' i,.; ."; 1'1'-
posed to bc able to est imatc IInkIIOl\' 1I illfol'III;11 il ' ll during its operation and uctel'lnillC all op!.i lll ;d (,01 1trol action from the est imated inforlllal.ion. \-:Xillllpies of work in t.hc arl'it iU'I' 1"11 (I!)i I) illld S;ll'idi~
(1977,1981). Expert Sysl.clllS Ilscd ;tS "II'-li l\('
I""I~
during process and con twl designs. ill COil till Ililtion with a control system computer-aided d" .,.;igll. a.re airnr:d t.o C.itpl. ll\'(' kll()\\'I(,t!g;" ;1j,"111 1111\\' I ll>' pro('I 'ss sholll" I,.. "1'.~lglII''' I11 111'.11'\' I" I'IIIliI \: 111 OilS ol)jecl.ivcs (cull!.I',,1 '11"dlll( "~' (·(·() II"III .' "1,') . ,,1101 to capture knowlcuge al)(>IIt pruc(~ss (,()llli~III · "II " I(. control specificat.ions and diff'erellt cOII!.l'ol ""slgll techniques (e.g. SISO cornpensation, 11111111\';11'1able design dc). Examples of work In t.l1(' ;11"';1 i\I'(' 1'01111<1 ill Fisher (1!J8(j); 'j'aylor (1!)X(j): \iida ;111.1 Umeda (1986); MacFarlcule cl Ill.
(I!Jt'i): 1,"\\1 11
and Morari (1988); Birky cl Ill. (IDBX)
Olh"1
(X-
arnple on plant-wide control sl.rillq~y pl ;lllnill .~ '" found in Stephanopou los cl al. (I DKi) ;\ 1110)1'( ' well-known expert systClI'1 applic;tlioll I11 I ""'·'· ... ,., control is to use all expert SYSt.CIII ;\.,.; ;1 1'111111,1(·-
An expert system can loosely be described as a computcr program that uses stored knowledge to r:l lllliatr: the problern solving behaviour of human experJ. ill some limitcd domain. A process control I'XP':1'1. Sysl.clIl is a lIal.llral ext.ensioll of itd-
ment to a conventional control SystCIII /'01' l'r()u'SS monitoring and alarm analysis. TIll' (" X[1<:1'1 systcm is rnainly used as all operator ilssiSl.illll. \\' 11\1·11 packages t.he kllowledgc 0[' cXI,criellc,~d ()llI'lilIIJI·'"
The development of
NIIIlICI'OIlS cXillllplcs of' difkl'l ' ll!. 111()IIII(l\'Ill~ ill'loIl catiolls can be /'oulld. i\ ['CIY eXiu llples <1\'(' \,,1-
slIch Cl system has been an important rcsearch area
son (1982) on nuclear power plan!.s , Sakaglll'lll illl"
in hot.h expe rt system and proccss control since 1111' "ilrly I ~)K()s. Expl'l'J. sysl.crns in process conl.rol
Matsumoto (1983) on electrical power SYSt.CIlI;< il llt! Palowit.ch nncl I\l'illlll'r (InK:») OIl rlll'lllir;t\ 1'1;11 h.
v,lIlccd control technology.
hill'I' I)( '(~ II inil.i;t!ly /'ocllsI'd Oil Ical'lling and s(~I/,
H.ccc llt.ly, it.iOllg with I.IIt' ilx;IIIi1I)illl.y
orgilllizing cont rol.
cial real-timc expert SystCl1I shells, 1IIIICll ill io'llll()11
319
,>I' C()IIIIIII'I'
has been paid to developing expert system to realtime process control, which is viewcd a.s expert control (EC) or knowledge-based control (KBC). In th e area the expert system is typically used on a small part of the plant (c.g., singlc closed loop) alld pcrforllwd <1.<; a slIpcrvisor to COllllllissioll and tune controllers, monitor pro cess, pcrforlll fault diagnosis, and restructure control systems, etc. Examples of such work can be found in Moore et al. (1984); Astrom et al. (1986); Arzen(1987); Karsai et al. (1987); Liu and Gertle (1987); Zhu and Dai (1988); Tzollana.s cl al. (1988); Whitlow and Debelak (1988) ; Basila and Cinar (1988). The work of the present paper belongs in this category. The goal in the long-run for expert control is to build an "intelligent" controller, which has increme nt a l learning capability and can be viewed as expert intelligcnt control (EIC). In this paper a prototype expert system using a Laguerre adaptive control technique, which IS a part of a long-term EC project, is presented. STRUCTURE OF EXPERT CONTROL Expert control is a very advanced and comprehensive branch of modern control technology. It , in fact, deals with a rathcr wide range of disciplines and technologies in both control theory and artificial intelligence (AI). In aspect of control theory, EC may consist of all basic control algorithms, e.g., PID control, auto-tuning, identificat.ion algorithms, adaptive cont.rol algorithms , dc., which constitute a toolbox of lIl11ncrical algorithms for different control strategies. The numerical algorithms provide explicit optimal solutions under some certain conditions. In aspect of artificial intelligence, EC may consist of knowledge base, knowledge representation, inference engine, data base, etc., which constitute a expert "brain" for emulating "intelligent" problem solving behaviours of human expert. In this part, the knowledge is presented symbolically and the problems presented are IIsua.lly fu:",,:y, cOll1plcx and difficlllt nlllllcrically to be solved; instead heuristic deduction and reasoning processes are used. EC is thus a combination of modern control technologies and artificial intelligence, and is in the frontie rs of modern science and tech no]ogy. The st.ruct IIrC of EC may ])(' shown as in Fig . 1. Control of a process basically involves two types of knowledge : control knowledge and proc ess knowkdgc. Control knowledge contains knowledge of alltomat.ic control, C • .rJ., knowlcdge about. ident.ification and different control strategies, etc. Process knowledge contains knowledge of the process that should be controlled, e.g., knowledge about
norma] and abnorlllal cO llditiollS , st.;II.ic ;II,d dy namic characteristics o f t.11I: proc",",s , cl c TI,i,; knowledge can be acquired through ol l-lill<' I'xl)('riments or from expericnced process ellgillf'l'I"s . III Fig . 1, the blocks of 1I111lf( ~ ric;r1 ;rl)!;orit 11111'; "lid <">1 1I.rollcrs cOlIsl.it.III.<' t.hl ' cO Ilt.ro l kll()",I"
320
A VAX Station 3100 running VMS and DEC Windows has been chosen as the hardware platform. The expert system shell G2 has been used for irnpl0.nwnt.ing t.h0. ~xp~rt . sysl.<~rn part. of t.he project. G2 is a real-timc expert syst.em shell with object-oriented knowledge representation. It provides schematics, dynamic models, and heuristics to represent the knowledge of applications . A standard int.erface GSI supplied along with G2 allows G2 to intcrface to external data sourccs including data acqllisition equipment, dal.iloascs, and ot.hcr extcrllal deviccs . The numerical algorit.hrns arc the main part of the software development in this project, alld consists of several packages of different algorithms about identification , adaptive control, 1'10 control, and managemcnt utility. Thcy are programmed in FORTRAN. A data communication bridge has been programmed in C to link the exp ert system G2 and the numerical algorithm packages. The G2 standard interface GSI provides specific functions for the communication purpose and is used to perform the development. A data acquisition equipment should be assembled to transfer data between the VAX-station and the actual controlled process . The expert system G2 and the numcrical algorithm packages can run on a single computer, as in our case, or on two separate computers. The measurements obtained from the process are transferred to the main computer VAX3100 and stored in data files. These data files constitute all exterllal database for the cxpert system and provide necessary information to both the expert system and the numerical algorithm packages. Such a system structure is aimed at matching the situations of hardware equipments commonly existing in the pulp and paper industries. LAGUERRE ADAPTIVE CONTROLLER Scveral adaptive control str a tegi es and id entification methods have been select.ed cHld used in the EC projcct in order t.o fulfil c1ifl"erent. reqlliremellts in a wider applications . COllsidering the rather wide range of different model structures and varying dead-times we have to deal with in industrial process cont.rol systems , the Laguerre unst.r1Ictured self-t.uner (LUST) mct.hod (Zcrvos and Dlllrlont, 1988) will first be implemented and tested. In the following a brief introduction of LUST is described .
process nOIse . An adaptive control scheme based on the ahove formlllat.ion uses t.he recllrsiv(~ le;\.<;t.-squar"s (Il.LS) met.hod t.o identify the parameter vect.or 1:. line, we use the exponential forgetting and resetting algorithm (EFRA) (Salgado et al., 1988), Theorems proving the global con vergellce alld stability of this scheme are present.ed in '/,crvos ;(11<1 Dlllllont (I D88). The choice of t.he pararnet.er l' in t.he L;(gllnr,' functions is not crucial. However, it inflltences the accuracy of the approximation of the plant. dYllalTlics as a truncated series. For Cl given plant , LilCrc exists an optimal p that minimizes the number or filters required to achieve a given accuracy. The chain of all-pass filters in the Laguerrc network provides good representation of a time delay T , ill particular when p 2N/T. The actual plant order hiL'; little bearing on the number of filters N. The horizon of the predictive control law is automatically adjusted on-line to assure closed-loop stability.
=
I3ased on theoretical analysis and on our illdustrial experience of the LUST algorithm, we knolV that LUST can perform an excellent control quality under the following conditions : • proper select. ion of the vallles for sampling interval T Laguerre time constant p number of Laguerre filters N • sufficient excitation for con vergent estimation of Laguerre spectrum gain vector c These factors should be fulfilled when a L liST con troller is used. IMPLEMENTATION
The limitations of the LUST algoirhtm chose n imply the following constraints on the process to be controlled , • sillgle-illPut and single-output. Sysl.<:1I1 • open-loop stable • well damped
The output of the plant yet) is described by, N
yet) =
L c;l;(t) + well = £6'l(t) + well
(I)
;=1
where
£6
[ll(t)
12(t)
[Cl
IN(t)
C2 ...
]T,
CN],
IT (t)
where the l;'s are the outputs from each Laguerre filt.er and wet) is the
The initial task is to commIssIon the LilgIWrr<' adaptive cOlltroller . For such a COllllllissioning, "n'eral characteristic factors of the process should initially be determined by testing the process or operator's input. In particular we require rough estimates of
321
Td 1~
Delay time H.cspollse or seUi IIg ti mc
the control law u(l) is shifted to be the ill(>llt. signal and the LUST controller is forlllillly cxccuft'd to control the process.
Because we then use difl'e rent rules whether the response is dominated by time delay or nol., we I.hen use the rollowing criterion ror discrilllinal.illg between the two cases: Time-delay No-delay
The procedure of commlsslonlllg is 1.11<'11 1'1'1'rormed in rules, r..9.,
Td/T> 0.25 Td/T < 0.25
where Td is the estimated dead-time and T is the dOlllillilllt I.illlc COlIst,lld, or I.he process and can bc calculatcd in a step rcsponse test that the time of the process response (without dead-time) reaches the point of 63% value of set-point, The operator is also asked whether the process is currclltly under a PlO cOlltrol 01' not. To determine above factors, a step response test is initially applied either in a close-loop (under an existing PID control) or in an open-loop (no PID control existed). Then delay time 'Id, response timc T, and time constant T are accordingly determined. Further the desired sampling interval T is calculated from the response time T, and the time delay T d . Thc number or Lagllerrc filters N alld constant time}J call then be calculated for thc followillg two cases (Dumont, Zervos ,~nd Pageau, 1990),
• Delay representation:
N
• IF process-status IS NO-PlO-EXIST TIII';i\ in order conclude that OPEN-LOOP-STEI'RESPONSE-TEST is TRUE; and set SETPOINT to INITIAL-SET-POINT; and inform the operator that "OPF:N LOOI' STF:I' H.F:SI'ONSE TEST is execut.illg". • IF process-status is YES-PID-EXIST TilE\, in order conclude that CLOSED-LOOPSTEP-RESPONSE-TEST is TRU 1::; and sct SET-POINT to INITIAL-Sf:T-I'OINT; i1l1d in fol'll I t.hc opcrator that "CLOSI':J) LOOI' STEP RESPONSE TEST is executillg". • IF process-status is NO-PID-EXIST; alld test-status is TEST-READY 'I'll EN ill Older set ADD-PRUS 1.0 I; alld set. ST,\H'I'LAGUERRE to 1; and inform the operator that "Initialization of Laguerre adapti ve controller, please wait." • IF proccss-st.atus is YI,;S-I'IJ)-I,;XIS'I': ;",d test-st.atus is TEST-REA IW 'I'll I':;-.J ill order set ADD-PRBS to 1; and set S'L\ InLAGUERRE to 1; and inform the ol'cr;ll<)I' that "Initialization of Laguel'l'e adclpt.i vc <:0 11troller, please wait." • IF estimate-status is GOOD 'I'll EN ill ()rder conclude that ADAPTIVE-CONTHOLREADY is TRUE; and set control-switch to (j; and inform the operator that " Laguerrc adaptive control is executing t"
T
> ~+1 T
• No delay: p= T
During the step response test (after obtaining proper values for Nand p), an identification experiment is performed for initialization of the LUST cont.roller. It is performed by sending a pseudo randOJll binary sigllal (P IU3S) tllat is addcd to the step signal. The Laguerre spectrum gain vector c is then estimated with open-loop signals. It means that the signals used as input signals in the estimation are I.hc comhined signals (st.ep signal plus PRBS), alld t.hat. the COlllPlltcd COli 1.1'01 laws n(l) from I.he LUST algorit.lllll arc, ill this case, 1101. yet used as the input signals to the process. After passing a period to allow near convergence of the estimator,
322
As mentioned previously the nUIllcrical illgorithm packages (written in FORTRAN) are IUI1ning parallel with the expert system G2, s01l1e control flags using digital number 0, 1, ... illT t.iI'ls 'I""d to pass COrrllll'
SIMULATION An example for commissioning and monitorillg the LUST controller used on a bleach-plant extraction stage in pulp mill has been si mula t.cd. ,\ process schemat.ic of the bleach-plilllt cxtr;I'" i011 st.age is developed 011 C 2 alld sho\\' 11 ill I: le-; :\ The objcct.s developcd for t.iw ap 1'1 ic ;If .ioll I1 ;1\1' physical instances which arc illu::;Lrut.cd ()II '1\1' schematic, and are knowledge represented by those
related attributes a nd rul es of G2, External se nso r s whi ch a r e r el ated to th e obj ec ts are develo ped Oil (;2 in o rd er 1.0 transfer data or rn es,~,,).!, "" 1)(' 1W"" II I II<' c'x t.,' rll;d pl' (),','~s illld th, ' ( ~ X1" '1' 1 "y" t.-III. 1'''1 1' si llllti;1I i0 11 pllrp u,s,',S , 1.11<' 1'1'0c,',"" is "lllll ia l, 'd I);.' it th co l'<'I i cal IlIo d el w hi c h is it fi r sl o r d er IlI o d el with tilll e delay, Th e procedlIrc o f CO IJ l llli ss io lling h as I )( ~ d esig ned to b e ab le 10 1'1111 Oil I.wo cases: :.IO-I'ID-EX I ST a.nd YES(,ID - EXIST , whi ch arc rc l at.cd t o OI'E N- LOOPS TEI' - I{ESI' O:.lS I~ - TEST alld C LOSED- LOOI'STLI' - IU:S I ' O.\SI ,:- TEST r('s p ec l ively, Th e CO II11'01 I
CONC L CS IO\S Tllis Pl'ot()t.yp" h ilS sll(I\\'11 P:'I'II ;d r" , ,,tih " I' :11, 1':<': proj ec t.. Ollly ti ll' cO IIIIIII""i u llil' ).!, par i " I' il L UST co ntrol using t il e ex p ert. sys t. c l ll l, :c hlJ iqll",,, has bee n intro du ce d in this p ape r , \I o nil o rin g fun ct io ns arc cllrren t ly bei ng d evei o pc 'd ,
M' I( .\ () \ \ ' 1.1,: 1)( ; 1-:\ 11,: :\ 'IS Th e finan cial supp ort o f lit e Scic lI (, " ( 'o'lll cil (, I' 13ritish Co lumbi a thr o ug h ST I) I.' (;r illll #~"- ':.'1.1 I grat.dull Y;L(, kn o wl ed ged,
Set po int PlO contro l ,~dJPli\' e control
way to t.ran sfer d ata b etwee n C a nd FORTRAN in th e VAX YMS en vi r o nment is through m a ilb ox, Th e kn ow l edge o f comm issi oning th e LUST co ntro ll er has b ee n rep r ese nted in both heur istics a lId in FOHTRAN, With cl ear and cert.ain obj ecti\'e i n mind , a n overall consi de r ati o n fo r opt ima l prog r arlIlIl ing to fulfil th e targe t, is n ecessary a nd sig llifi cant , It means that if th e kn owledge can be easily represented in a numerical way, then it may not be necessary 10 IJ.~" Il cl lri st.i('s, Af't' ~ r d eve lo plIlcllL o f' I he knowledge r ep rcse lIt. a t.i on fo r th e sp ec ifi c p r ocess, \\'hic h co n tailIs the d eve lopme nts o f obj ects and nIl !'s, th e expe rt. sy st. em G2 is t hc n r eady t o ('o nl r o l 1111' pr ()c!'ss with its fill ed ex perti sc, 1'1'0cc- dures for I Il c cO lIlllli ss io nill g ;lIld Ill o lliLo rin g tllC I TS T co nI r o ller have bee ll d( ~ s igll c d \ViLh g uicl ed st eps,
Expert System
Fig, I, Stru ct ur e o f Ex pc rt. CO lltl'o l
- -- - - _ ' _ _ ,: C'0ntrc.llel's , ,i - - , , 7,'<:'" ,. - - - - "
I'
I.
c, t o a ll sw er th e Il ecessary qu es tions
\\'lli ('h o pr'l'a l or Ci ln rHo\'ici c (e,g , th e p r ocess IS CIIJ'J" 'l ll ly 1IIId,' 1' a 1' 1i) CO IIII'(l l" ), Afkl' illlSW Cl'Illg Ill<'sc '1 II c,, 1lUll" lll c expe rt SySl<: 111 ca ll auto lll at ica lly pe r fo rm it co rr ec t. proce dure fo r co rnmisslo nlllg and t o m Ollitor t he per fo rman ces o f th e kn o wl eclge, Sirnulations co ntro l \\'Ith its exp ert 0 11 I Iw ilppli c;11 iOIl havc ShO\\' 11 a I' ilt. her co nvin cing r"~ltits () II "XP'; I't. sllp c r v iso l'Y to t.h e CO lJllllissi o llillg;
pr oce dlll'c and Illo nito rin g capability.
PROCESS
I: i
----~~--~,
Data
Acqu lsltt cn
i
,
i.'_ ' :..,- --
: t
"
sz
eri~31
'i urr..
.
(
lg_tr_i_tL_II:_ 8_ ' _ _ A_
"
KnOS"'~dge I ,~
~~
Data " '",,_ _-::B~a,ge_ _--,pA,1 _ _E-,.3""se_ __
..;'::> !
j
"", 7
Inferen ce En/i:ine
Cse:r
lnlerfuce
Fig, 2, SystClI1 Stl'lI ctlll'r
323
r\
j : 1 '1
,~-----~I
T lIe o pe r ato r is gUided thro ug h t he initial
proced ur es ,
I
i\.,j
'1
-
6,
[o'u, K .S. (1971). Learllillg cOlIl.rol sYSL('111 alld intelligent control system - ... All illtrodllcl.ioll of artificial intelligent and automatic control. IEEE '!'rans" AC- 16, 70 - 72 . .Jackson, P. (198G). [lIl.rocillcl.ion 1.0 I':xpnl. Systems. Addison-Wesley: Readillg , MA. Karsai G ., E . Blokland, C. Biegl, .J . Sztipanovits, K. Kawamura, N. Miyasaka and M. [lIl1i (1987). Intelligellt supervisory cOlIl.roll<-!· for gas distriblltioll sysl.(~1\1. I'ro(' . I~)KI All\, Con trol Conf., 1353-1358. Lewin, D .R . and M. Morari (1988). ROBEX: an expert system for robust controller synthesis. Comput. Chem. Eng., l2., 1187-1198. ,\ slIperLiu, K . and J. Gertler (1987). visory (expert) adaptive control scheme. IFAC 10th World Congress. Munich. MacFarlane A.G.L. , G. Gruebel and .J. ,\ckcrmann (1987). Future design cnviWllllWllts for control engineering. IFAC 10th World Congress. Munich.
Fig. 3. Bleach-Plant Extraction Stage
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