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PLC的液位控制系统实际,机械毕业设计全套
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Control Engineering PracticeembeddedVrancLjubljana,bNova Gorica Polytechnic, Nova Gorica, Sloveniaidentication steps to provide reliable operation. The controller monitors and evaluates the control performance of the closed-loopsystem. The controller was implemented on a programmable logic controller (PLC). The performance is illustrated on a eld testin industrial applications, as summarised below:ARTICLE IN PRESS/locate/conengpracC3Corresponding author. Tel.: +38614773994;1. Because of the diversity of real-life problems, a singlenonlinear control method has a relatively narrow0967-0661/$-see front matter r 2005 Elsevier Ltd. All rights reserved.doi:10.1016/j.conengprac.2005.05.006fax: +38614257009.E-mail address: samo.gerksicijs.si (S. Gerksic).application for control of pressure on a hydraulic valve.r 2005 Elsevier Ltd. All rights reserved.Keywords: Control engineering; Fuzzy modelling; Industrial control; Model-based control; Nonlinear control; Programmable logic controllers; Self-tuning regulators1. IntroductionModern control theory offers many control methodsto achieve more efcient control of nonlinear processesthan provided by conventional linear methods, takingadvantage of more accurate process models (Bequette,1991; Henson & Seborg, 1997; Murray-Smith &Johansen, 1997). Surveys (Takatsu, Itoh, & Araki,1998; Seborg, 1999) indicate that while there is aconsiderable and growing market for advanced con-trollers, relatively few vendors offer turn-key products.Excellent results of advanced control concepts, basedon fuzzy parameter scheduling (Tan, Hang, & Chai,1997; Babuska, Oosterhoff, Oudshoorn, & Bruijn,2002), multiple-model control (Dougherty & Cooper,2003; Gundala, Hoo, & Piovoso, 2000), and adaptivecontrol (Henson & Seborg, 1994; Ha gglund & Astrom,2000), have been reported in the literature. However,there are several restrictions for applying these methodsdINEA d.o.o., Ljubljana, SloveniaeComputer Technology Institute, Athens, GreecefUniversity of Chemical Technology and Metallurgy Sofia, Sofia, BulgariaReceived 23 April 2004; accepted 15 May 2005AbstractThis paper presents an innovative self-tuning nonlinear controller ASPECT (advanced control algorithms for programmable logiccontrollers). It is intended for the control of highly nonlinear processes whose properties change radically over its range ofoperation, and includes three advanced control algorithms. It is designed using the concepts of agent-based systems, applied with theaim of automating some of the conguration tasks. The process is represented by a set of low-order local linear models whoseparameters are identied using an online learning procedure. This procedure combines model identication with pre- and post-cUniversity of Ljubljana, Faculty of Electrical Engineering, Ljubljana, SloveniaAdvanced control algorithmslogic controllerSamo Gerksica,C3, Gregor Dolanca, DamirSaso Blazicc, Igor Skrjancc, Zoran MarinsRobert Kinge, Mincho HadjiskiaJozef Stefan Institute,in a programmableica, Jus Kocijana,b, Stanko Strmcnika,ekd, Miha Bozicekd, Anna Stathakie,f, Kosta BoshnakovfSlovenia () ntsfriendmaticindustfromling,procedcontroller monitors the resulting control performanceARTICLE IN PRESSa nonlinear process model. The model is obtainedoperating process signals by experimental model-using a novel online learning procedure. ThisThefromfor implementation on PLC or open controllerrial hardware platforms.controller parameters are automatically tunedfeatuadaptedssioning of the controller is simplied by auto-experimentation and tuning. A distinguishingre of the controller is that the algorithms aremetecommiThe ASPECT controller is an efcient and user-ly engineering tool for implementation of para-r-scheduling control in the process industry. Theused,the sensor readings, specic hardware platforms areetc.is demandedtoeld of application. Therefore, more exible methodsor a toolbox of methods are required in industry.2. New methods are usually not available in a ready-to-use industrial form. Custom design requires consider-able effort, time and money.3. The hardware requirements are relatively high, due tothe complexity of implementation and computationaldemands.4. The complexity of tuning (Babuska et al., 2002) andmaintenance makes the methods unattractive tononspecialised engineers.5. The reliability of nonlinear modelling is often inquestion.6. Many nonlinear processes can be controlled using thewell-known and industrially proven PID controller.A considerable direct performance increase (nancialgain) is demanded when replacing a conventionalcontrol system with an advanced one. The main-tenance costs of an inadequate conventional controlsolution may be less obvious.The aim of this work is to design an advancedcontroller that addresses some of the aforementionedproblems by using the concepts of agent-based systems(ABS) (Wooldridge & Jennings, 1995). The mainpurpose is to simplify controller conguration by partialautomation of the commissioning procedure, which istypically performed by the control engineer. ABS solvedifcult problems by assigning tasks to networkedsoftware agents. The software agents are characterisedby properties such as autonomy (operation withoutdirect intervention of humans), social ability (interactionwith other agents), reactivity (perception and responseto the environment), pro-activeness (goal-directed be-haviour, taking the initiative), etc. This work does notaddress issues of ABS theory, but rather the applicationof the basic concepts of ABS to the eld of processsystems engineering. In this context, a number of limitshave to be considered. For example: initiative isrestricted, a high degree of reliability and predictability, insight into the problem domain is limitedS. Gerksic et al. / Control Engineerin2ure is based on model identication using theand reacts to detected irregularities.The controller comprises the run-time module (RTM)and the conguration tool (CT). The RTM runs on aPLC, performing all the main functionality of real-timecontrol, online learning and control performancemonitoring. The CT, used on a personal computer(PC) during the initial conguration phase, simplies theconguration procedure by providing guidance anddefault parameter values.The outline of the paper is as follows: Section 2presents an overview of the RTM structure anddescribes its most important modules; Section 3 givesa brief description of the CT; and nally, Section 4describes the application of the controller to a pilotplant where it is used for control of the pressuredifference on a hydraulic valve in a valve test apparatus.2. Run-Time ModuleThe RTM of the ASPECT controller comprises a setof modules, linked in the form of a multi-agent system.Fig. 1 shows an overview of the RTM and its mainmodules: the signal pre-processing agent (SPA), theonline learning agent (OLA), the model informationagent (MIA), the control algorithm agent (CAA), thecontrol performance monitor (CPM), and the operationsupervisor (OS).2.1. Multi-faceted model (MFM)The ASPECT controller is based on the multi-facetedmodel concept proposed by Stephanopoulus, Henning,and Leone (1990) and incorporates several model formsrequired by the CAA and the OLA. Specically, theMFM includes a set of local rst- and second-orderlocal learning approach (Murray-Smith & Johansen,1997, p. 188). The measurement data are processedbatch-wise. Additional steps are performed before andafter identication in order to improve the reliability ofmodelling, compared to adaptive methods that userecursive identication continuously (Ha gglund & As-trom, 2000).The nonlinear model comprises a set of local low-order linear models, each of which is valid over aspecied operating region. The active local model(s) isselected using a congured scheduling variable. Thecontroller is specically designed for single-input, single-output processes that may include a measured dis-turbance used for feed-forward. Additionally, theapplication range of the controller depends on theselected control algorithm. A modular structure of thecontroller permits use of a range of control algorithmsthat are most suitable for different processes. Theg Practice () discrete-time linear models with time delay and offset,ntsARTICLE IN PRESSS. Gerksic et al. / Control Engineerinwhich are specied by a given scheduling variable s(k).The model equation of rst order local models isyk 1C0a1; jykb1; juk C0dujc1; jvk C0dvjrj,(1)while the model equation of second order models isyk 1C0a1; jykC0a2; jyk C01b1; juk C0duj b2; juk C01C0dujc1; jvk C0dvj c2; jvk C01C0dvjrj, 2where k is the discrete time index, j is the number of thelocal model, y(k) is the process output signal, u(k) is theprocess input signal, v(k) is the optional measureddisturbance signal (MD), du is the delay in the modelbranch from u to y,dv is the delay in the model branchfrom v to y, and ai,j, bi,j, ci,jand rjare the parameters ofthe jth local model.The set of local models can be interpreted as aTakagiSugeno fuzzy model, which in the caseof a second order model can be expressed in theFig. 1. Run-time moduleg Practice () 3following form:yk 1C0Xmj1bjka1; jykC0Xmj1bjka2; jyk C01Xmj1bjkb1; juk C0dujXmj1bjkb2;juk C01C0dujXmj1bjkc1; jnk C0dnjXmj1bjkc2; jnk C01C0dnjXmj1bjkrj,3where bj( k) is the value of the membership function ofthe jth local model at the current value of the schedulingvariable s(k). Normalised triangular membership func-tions are used, as illustrated in Fig. 2.overview.ntsARTICLE IN PRESSThe scheduling variable s(k) is calculated usingcoefcients kr, ky, ku,andkv, using the weighted sumskkrrkkyykkuuk C01kvvk. (4)The coefcients are congured by the engineer accord-ing to the nature of the process nonlinearity.2.2. Online Learning Agent (OLA)The OLA scans the buffer of recent real-time signals,prepared by the SPA, and estimates the parameters ofthe local models that are excited by the signals. Themost recently derived parameters are submitted to theMIA only when they pass the verication test and areproved to be better than the existing set.The OLA is invoked upon demand from the OS orautonomously, when an interval of the process signalswith sufcient excitation is available for processing. Itprocesses the signals batch-wise and using the locallearning approach. An advantage of the batch-wiseconcept is that the decision on whether to adapt themodel is not performed in real-time but following adelay that allows for inspection of the identicationresult before it is applied. Thus, better means for controlover data selection is provided.A problem of distribution of the computation timerequired for identication appears with batch-wiseprocessing of data (opposed to the online recursiveprocessing that is typically used in adaptive controllers).This problem is resolved using a multi-tasking operationsystem. Since the OLA typically requires considerablyFig. 2. Fuzzy membership functions of local models in the MFM.S. Gerksic et al. / Control Engineerin4more computation than the real-time control algorithm,it runs in the background as a low-priority task.The following procedure, illustrated in Fig. 3,isexecuted when the OLA is invoked.2.2.1. Copy signal bufferThe buffer of the real-time signals is maintained bythe SPA. When the OLA is invoked, the relevant sectionof the buffer is copied for further processing.2.2.2. Excitation checkA quick excitation check is performed at the start, sothat processing of the signals is performed only whenthey contain excitation. If the standard deviations of thesignals r(k), y(k), u(k), and v(k) in the active buffer arebelow their thresholds, the execution is cancelled.2.2.3. Copy active MFM from MIAThe online learning procedure always compares thenewly identied local models with the previous set ofparameters. Therefore, the active MFM is copied fromthe MIA where it is stored. A default set of modelparameters is used for the local models that have not yetbeen identied (see Section 2.3).2.2.4. Select local modelsA local model is selected if the sum of its membershipfunctions bj(k) over the active buffer normalised bythe active buffer length exceeds a given threshold.Only the selected local models are included in furtherprocessing.2.2.5. IdentificationThe local model parameters are identied using thefuzzy instrumental variables (FIV) identication methoddeveloped by Blazic et al. (2003). It is an extension of thelinear instrumental variables identication procedure(Ljung, 1987) for the specied MFM, based on the locallearning approach (Murray-Smith & Johansen, 1997).The local learning approach is based on the assumptionthat the parameters of all local models will not beestimated in a single regression operation. Compared tothe global approach it is less prone to the problems ofill-conditioning and local minima.This method is well suited to the needs of industrialoperation (intuitiveness, gradual building of the non-linear model, modest computational demands). Itenables inventory of the local models that are notestimated properly due to insufcient excitation. It isefcient and reliable in early conguration stages, whenall local models have not been estimated yet. On theother hand, the convergence in the vicinity of theoptimum is slow. Therefore, it is likely to yield a worsemodel t than methods employing nonlinear optimisa-tion. The following briey describes the procedure.Model identication is performed for each selectedlocal model (denoted by the index j) separately. Theinitial estimated parameter vectorhj;MIAis copied fromthe active MFM, and the covariance matrix Pj,MIAisinitialised to 105I (identity matrix). The FLS (fuzzyleast-squares) estimates,hj;FLSand Pj,FLS, are obtainedusing weighted least-squares identication, with bj(k)used for weighting. The calculation is performedrecursively to avoid matrix inversion. The FIV (fuzzyinstrumental variables) estimates,hj;FIVand Pj,FIV, arecalculated using weighted instrumental variables identi-cation.In order to prevent result degradation by noise, ag Practice () dead zone is used in each step of FIV and FLS recursiventsARTICLE IN PRESSS. Gerksic et al. / Control Engineerinestimation. The vector of parameters and the covariancematrix are updated only if the absolute weighteddifference between the process output and its predictionis above the congured noise threshold.Fig. 3. Online learningg Practice () 5In case of lack of excitation in the branch from u to yor in the model branch from v to y (or when measureddisturbance is not present at all), variants of the methodwith reduced parameter estimate vectors are cedure.ntsC15wiARTICLE IN PRESS2.2.6. Verification/validationThis step is performed by comparing the simulationoutput of a selected local model with the actual processoutput in the proximity of the local model position. Thenormalised sum of mean square errors (MSEj)iscalculated. The proximity is dened by the membershipfunctions bj. For each of the selected local models, thisstep is carried out with three sets of model parameters:hj;MIA;hj;FLS; andhj;FIV: The set with the lowest MSEjisselected.Then, global verication is performed by comparingthe simulation output of the fuzzy model including theselected set with the actual process output. The normal-ised sum of mean square errors (MSEG) is calculated. Ifthe global verication result is improved compared tothe initial fuzzy model, the selected set is sent to theMIA as the result of online learning, otherwise theoriginal sethj;MIAremains in use.For each processed local model, the MIA receives theMSEj, which serves as a condence index, and a agindicating whether the model is new or not.2.2.7. Model structure estimationTwo model structure estimation units are alsoincluded in the OLA. The dead-time unit (DTU)estimates the process time delay. The membershipfunction unit (MFU) suggests whether a new localmodel should be inserted. It estimates an additionallocal model in the middle of the interval between the twoneighbouring local models that are the most excited. Themodel is submitted to the MIA if the global validationof the resulting fuzzy model is sufciently improved,compared to the original fuzzy model.2.3. Model Information Agent (MIA)The task of the MIA is to maintain the active MFMand its status information.Its primary activity is processing the online learningresults. When a new local model is received from theOLA, it is accepted if it passes the stability test and itscondence index is sufcient. If it is accepted, a readyfor tuning ag is set for the CAA. Another agindicates whether the local model has been tuned sincestart-up or not. If the model condence index is verylow, the automatic mode may be disabled.The MIA contains a mechanism for inserting addi-tional local models into the MFM. This may occureither by request or automatically, using the MFU ofthe OLA. The MIA may also store the active MFM to alocal database or recall a previously stored one, which isuseful for changing of modes.At initial conguration, the MIA is lled with defaultlocal models based on the initial estimation of theprocess dynamics. They are not exact but may provideS. Gerksic et al. / Control Engineerin6reliable (although sluggish) control performance, similarprocedure of the controller parameters from theMFM when the MIA reports that a new local modelis generated if auto-tuning is enabled. The parametersof the control layer and the scheduling layer arereplaced in such a manner that real-time control isnot disturbed.Three CAAs have been developed and each has beenproved effective in specic applications: the Fuzzyparameter-scheduling controller (FPSC), the dead-timecompensation controller (DTCC), and the rule-basedneural controller (RBNC). In this paper
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