Modeling, Identification and Control, Vol. 37, No. 3, 2016, pp. 159–169, ISSN 1890–1328

A metamorphic controller for plant control systemdesign

T. Klopot 1 P. Skupin 1 D. Choinski 1 R. Cupek 1 M. Fojcik 2

1Faculty of Automatic, Electronics and Computer Science, Silesian University of Technology, ul. Akademicka 16,44-100 Gliwice, Poland. E-mail: {tomasz.klopot, piotr.skupin, dariusz.choinski, rafal.cupek}@polsl.pl

2Faculty of Engineering and Science, Sogn og Fjordane University College, Svanhaugevegen 1, 6812 Førde, Norway.E-mail: marcin.fojcik@hisf.no

Abstract

One of the major problems in the design of industrial control systems is the selection and parameteri-zation of the control algorithm. In practice, the most common solution is the PI (proportional-integral)controller, which is simple to implement, but is not always the best control strategy. The use of moreadvanced controllers may result in a better efficiency of the control system. However, the implementationof advanced control algorithms is more time-consuming and requires specialized knowledge from controlengineers. To overcome these problems and to support control engineers at the controller design stage, thepaper describes a tool, i.e., a metamorphic controller with extended functionality, for selection and imple-mentation of the most suitable control algorithm. In comparison to existing solutions, the main advantageof the metamorphic controller is its possibility of changing the control algorithm. In turn, the candidatealgorithms can be tested through simulations and the total time needed to perform all simulations canbe less than a few minutes, which is less than or comparable to the design time in the concurrent designapproach. Moreover, the use of well-known tuning procedures, makes the system easy to understand andoperate even by inexperienced control engineers. The application was implemented in the real industrialprogrammable logic controller (PLC) and tested with linear and nonlinear virtual plants. The obtainedsimulation results confirm that the change of the control algorithm allows the control objectives to beachieved at lower costs and in less time.

Keywords: model-based design, parallel design, programmable logic controller, control system design,simulation

1 Introduction

In order to achieve reproducible product properties,but also to minimize production costs and the influ-ence of human factor, which can be a source of errors,it is necessary to design and use control systems. Itis especially important for continuous processes in thechemical, biotechnological or food industry, where themain task of control systems is to maintain key processparameters at desired levels.

The synthesis of the control system requires coop-

eration between a process engineer and control engi-neers (experts), because the experts, being more expe-rienced, posses sufficient knowledge to determine thecontroller structure and to tune its parameters in or-der to fulfill the control objectives (Groover, 2007; Se-borg et al., 2010). In practice, the choice of the controlalgorithm depends on the simplicity of its implementa-tion and the required knowledge of its operation. Forthese reasons, the most common industrial controlleris the PID (proportional-integral-derivative) controlleror its simplified form (the PI controller) without the

doi:10.4173/mic.2016.3.2 c© 2016 Norwegian Society of Automatic Control

Modeling, Identification and Control

derivative action (Li et al., 2006b; Kasprzyczak andMacha, 2008, 2015; Yu, 2006). In the case of more ad-vanced control algorithms such as Fuzzy Logic Control(Budzan and Wyzgolik, 2014), B-BAC (Balance-BasedAdaptive Control) (Czeczot, 2006) or a class of predic-tive controllers (e.g., DMC Dynamic Matrix Control orPFC Predictive Functional Control) (Laszczyk, 2001;Laszczyk et al., 2013), it is possible to achieve higherquality of control than in the case of using the classicalPID controller. In other words, the formulated controlobjectives can be achieved at lower costs and in lesstime. However, the implementation of the advancedcontrollers is more difficult and requires more exten-sive knowledge (Figure 1a). One of the possibilities isto find an expert that is able to choose, implement andtune the most suitable control algorithm as the alter-native to the classical PI controller. But in majorityof cases, the experts usually specialize only in one typeof the controller. Hence, to implement various con-trol algorithms, it would be necessary to employ moreexperts. Depending on the control objectives and spec-ified constraints, the experts would have to cooperatewith each other to determine which type of the controlalgorithm to choose and how to tune its parameters(Figure 1b).

A great advantage of this approach is that theoptional control algorithms can be implemented andtested concurrently based on a mathematical model ofthe controlled plant (Figure 1b). As a result, it is pos-sible to reduce design time and to obtain the optimalcontroller structure.

However, such solution is often unacceptable in in-dustrial practice because hiring more experts increasesthe overall costs. In effect, the performance of themajority of industrial control systems is not optimal(Eriksson and Isaksson, 1994; Hagglund, 1995; Huanget al., 1997).

Currently, the simulation techniques are becomingmore popular in the design of control systems (Groover,2007; Stebel and Metzger, 2012). Especially usefulis the virtual commissioning technology in the designof manufacturing systems (Fratczak et al., 2013; Kimet al., 2011; Ko et al., 2013a,b; Ko and Park, 2014;Koo et al., 2011; Lee and Park, 2014), but also con-tinuous processes (Barth and Fay, 2013; Bradu et al.,2009; Geist et al., 2013; Gerlach et al., 2013), wherea real controller is tested through simulations with avirtual plant. Another possibility is to use supportingapplications and tools that are offered by many man-ufacturers of the control equipment. One of the maintasks of these tools is to tune the controller accordingto some rules for the desired operating point (Li et al.,2006a) as shown, for instance, in Super Control soft-ware proposed by Yokogawa Electric Corporation that

is based on fuzzy logic (Wilson and Callen, 2004), orModular PID Control proposed by Siemens that pro-vides tools for optimal parameterization of the PIDcontroller.

However, the functionalities of these tools are usu-ally limited to the parameterization of one type of con-troller, usually the PID control algorithm, with nopossibilities of changing its structure. Because thestiff controller structure may limit the performanceof the whole control system, therefore, the possibilityof switching between various control algorithms mayresult in a better quality of control and lower costs.For these reasons, the multifunctional software PC7Siemens, for example, provides supporting tools for im-plementation of both the PID controller and model pre-dictive control (MPC) algorithm. Nonetheless, it doesnot support the control engineer in the selection of thecontrol algorithm. In turn, to reduce design time andcosts, the tool has to provide possibilities for paralleldesign as in the group of cooperating experts.

To face these problems, a tool, i.e., a metamorphiccontroller with extended functionality, is proposed inthis paper that can replace the group of cooperatingexperts (Figure 1c). In effect, only one control engineeroperating the metamorphic controller and cooperatingwith the process engineer, is enough to select the opti-mal controller. Moreover, the control engineer does nothave to possess knowledge on how to implement andhow to select the optimal control algorithm for a de-sired operating point of the process. The presentedtool can also be helpful in normal operation of theplant, whenever the operating point of the process hasto be changed. The proposed solution has been imple-mented and tested on a real industrial programmablelogic controller (PLC). The next section describes thebasic properties of the supporting software and its rolein the control system design. The third section presentsthe idea of the metamorphic controller and the fourthsection gives the implementation details. The fifth sec-tion presents the effectiveness of the proposed solutionin comparison to the classical control algorithm (thePI controller). Finally, conclusions and future worksare presented in the last section of the paper.

2 The role of supporting tools inthe control system design

One of the basic tasks of each control system is mini-mization or elimination of the control error, which is adifference between set point (SP) and process variable(PV) of a controlled plant. For instance, in the tem-perature control of water inside a tank, the controlledplant is the tank itself, PV is the temperature mea-

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Figure 1: The selection of the controller structure and its parameterization at the controller design stage: a)comparison of the available controllers; b) selection of the optimal controller by cooperation of experts;c) metamorphic controller as a tool for selection of the optimal controller structure.

sured inside the tank, and SP is a desired temperatureof water. The controller will be changing the temper-ature, for example, by means of an electric heater, tominimize the control error. As mentioned in the intro-duction, the controller selection and its parameteriza-tion are fundamental problems at the controller designstage. Hence, the manufacturers of control equipmentoffer additional tools and supporting applications (in-tended mainly for the PID controllers) to facilitate thedesigning process.

To make the supporting tool useful and easy in op-eration for a wide spectrum of users, it must be basedon well-known controller design methods. Therefore,most of these tools use a mathematical model of thecontrolled plant, which can be determined from massand energy balances or, in a simplified manner, fromstep response of the plant for a given operating point(Gyongy and Clarke, 2006). The latter approach ismore preferable by control engineers, since the math-ematical description of the controlled plant is simpli-fied, can be easily derived, and is well-understood, evenby inexperienced control engineers. In this case, awide class of continuous industrial processes can bedescribed by the first order plus time delay (FOPTD)model (Seborg et al., 2010) in the form of transfer func-tion:

K(s) =ke−sT0

sT + 1(1)

where: s - complex variable in the Laplace domain,T - overall time constant, T0 - time delay, k - plantgain.

The overall time constant T determines the responsetime of the plant to a change in its input signals, while

the time delay T0 determines the delay in response tothe input signals. In turn, the plant gain k describesstatic properties of the controlled plant. For instance,in the water tank example, the larger the volume ofwater, the larger the overall time constant. In otherwords, it takes more time to reach new steady stateor another operating point of the system, i.e., the newtemperature of water followed by an increase or de-crease of the heating power. And, if the plant gain issmall it means that the water tank system will requiremore heating power to increase the temperature of wa-ter. Because these parameters describe static and dy-namic behavior of the plant, they are crucial in param-eterization of the controller, and the FOPTD model(1) is the basis for most tuning procedures that arebased on the step response of the plant. It should beemphasized here that the FOPTD model is a linearapproximation of the nonlinear plant around an oper-ating point of the system, which is strictly determinedby the input and output signals of the plant. There-fore, the parameter values in (1) may significantly dif-fer for other operating points of the system. In turn,the choice of the operating point is dependent on thecurrent control goals and technological requirements.

Irrespective of the selected control algorithm, the be-havior of the control system is dependent on the con-troller parameters. Figure 2 presents three typical re-sponses of the control system to a step change in theSP for three various sets of the controller parameters.

The maximum difference between PVmax and a newsteady state value defines the overshoot value (Figure2), which can be used as a performance criterion inthe controller designing problem, for instance, no over-

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Figure 2: Possible responses of the control system to astep change in the SP for three various setsof the controller parameters.

shoot or overshoot less than 25%. Another importantparameter is the settling time, which is defined as atime needed to achieve a new steady state with a spec-ified accuracy (Figure 2). The settling time can also beused in controller tuning procedures. Hence, dependingon the controller parameters, a new steady state canbe achieved either quickly with overshoots (damped os-cillations), which is more energetically expensive andincreases wear on control equipment, or without over-shoots (non-oscillatory response) for longer settlingtimes. The critical case is a borderline case betweenthe oscillatory and non-oscillatory responses.

If the control system behavior and its performancecan be described mathematically in a simple way, thenthe supporting tool will allow for use of the advancedcontrol algorithms without having extensive knowledgein the field. Additionally, if the candidate control algo-rithms can be tested concurrently, then the supportingtool can replace the group of cooperating experts asshown in Figure 1. The answer is a metamorphic con-troller with extended functionality that allows for se-lection of the optimal control algorithm depending onthe current control goals. The idea of the metamorphiccontroller and implementation details on the industrialPLC are presented in the next sections.

3 The idea of metamorphiccontroller

In comparison to existing supporting tools, the mainadvantage of the metamorphic controller is its possibil-

ity of changing the control algorithm. In the literature,the problem of switching between several controllersis generally known. For example, metamorphic con-trollers developed for the manufacturing systems havebeen described in Balasubramanian et al. (2001); Xuet al. (2002). In this case, the adaptation of the con-troller structure (its metamorphosis) results from con-stant changes in the manufacturing environment. Theselection of a proper control algorithm can also be re-alized with the help of fuzzy logic rules. Such an ap-proach was proposed in Abdullah et al. (2008) for au-tonomous vehicle control. In turn, the selection of themost suitable control algorithm presented in Paul et al.(2005); Wang et al. (2007) was realized by minimizing acriterion function. In the presented paper, the selectionof the control algorithm depends on the desired oper-ating point and behavior of the control system (Figure2).

The understanding of the control system behaviorallows for translating the control objectives and con-straints, such as energy expenditure, into a mathemat-ical language which can be understood by the meta-morphic controller. This can be achieved by means ofperformance indices, which characterize the behavior ofthe control system. In the presented case, three basicperformance indices were taken into account: IAE (In-tegral of the Absolute Error), ITAE (Integral of Timemultiplied by the Absolute Error), ISE (Integral SquareError) and their definitions are as follows (Davendraet al., 2010):

IAE =

∫ Tf

0

|e(t)|dt, ITAE =

∫ Tf

0

t · |e(t)|dt,

ISE =

∫ Tf

0

e2(t)|dt (2)

where: e – control error at instant t, Tf – final instant(dependent on the settling time).

The IAE index ensures small overshoots, the ITAEgives similar results as IAE, but weights error with timeand ensures fast transients, i.e., relatively short settlingtimes, in turn, the ISE index is a compromise betweensufficiently fast transients and small overshoots (Dav-endra et al., 2010).

Depending on the control objectives and constrains,the idea is to test several candidate controllers with theFOPTD plant model (Figure 3). Then, the metamor-phic controller selects the algorithm associated withthe smallest value of the performance index.

As in many existing tools and applications, the meta-morphic controller uses the FOPTD model (1) in tun-ing procedures. Figure 4 presents the basic units ofthe metamorphic controller that extend its function-ality. The additional function units are responsible

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Figure 3: The idea of the metamorphic controller asthe tool supporting control engineers.

for: identification of parameters of the FOPTD model,controller parameterization, simulations of the controlsystem with each individual controller and calculationof the performance indices. In the presented case, themetamorphic controller uses three various control algo-rithms: PI controller (the most common in practice),DMC controller (effective for plants with variable timedelay) and B-BAC controller (effective in attenuationof disturbances). In order to compare the effectivenessof the control systems with various control algorithms,the controllers were tuned as follows. The tuning pa-rameters of each control algorithm were determined byusing the FOPTD model of the nonlinear plant. ThePI controller was tuned according to the well-knownCohen-Coon rules resulting in fast transient responsesof the closed-loop system. The B-BAC controller wastuned according to the same rules. It was possible, be-cause B-BAC controller uses tuning parameters thatare equivalent to the parameters of the PI controller(see, e.g., (Laszczyk et al., 2012; Laszczyk and Czeczot,2012)). In turn, for the DMC algorithm, it is not possi-ble to find equivalent parameters. The DMC controllerbelongs to the family of predictive controllers and hasa completely different structure than the PI controller.In this case, the tuning parameters of the DMC al-gorithm were chosen according to the rules given in(Laszczyk et al., 2013) to obtain fast responses of theclosed-loop system that are similar to the responses ofthe control system with the PI controller.

4 Implementation of themetamorphic controller in thePLC

The application of the metamorphic controller was im-plemented and tested in the industrial PLC SiemensS7-300 series (Szczypka, 2011). The interaction withthe metamorphic controller is realized by means of ahuman machine interface (HMI) application created inthe Siemens ProTool software installed on a computer

and connected to the real PLC via the Ethernet net-work. The implemented application is composed ofseveral functions (FC) and function blocks (FB) ac-cording to the IEC 61499 standard (Vyatkin, 2012),which is suitable for the design of distributed controlsystems and multi-agent-based systems. In the pre-sented case, the functions may contain function blockswith additional DataBlocks (DB) to store parametersvalues for the next call of the FB. According to the IEC61499 standard, there is a distinction between data andevent inputs in each function or function block. For in-stance, once the calculations in FC1 are finished, thefunction FC8 receives the acknowledgement signal fromFC1 and data (Figure 5).

Figure 5: The controller selection procedure.

At the beginning of the whole procedure, the controlengineer specifies a new operating point (a SP value) ofthe plant, the performance index, and the initial ampli-tude of the CV signal for model identification purposes.Then, the controller selection procedure, which is a se-quence of consecutive steps (Figure 5), starts. Figure6 presents the list of all functions and function blocksused in the implemented application.

Step 1. Initialization of parameters – parameters en-tered by the control engineer are assigned to vari-ables in the function FC6.

Step 2. Step response of the plant – the function FC2uses the initial amplitude of the CV to obtain thestep response of the plant. At controller designstage, the step response can be obtained from themathematical model of the plant. During normaloperation of the control system, the step responsecan be obtained either from the real plant or fromits model. The former case gives accurate data,but due to long settling times (even up to sev-eral hours), this approach may be useless in prac-tice. In this paper, the real plant is replaced by itsmathematical model and implemented by meansof function blocks: FB3 (model of delay), FB7

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Figure 4: The general scheme of the metamorphic controller and its connection with the plant.

(first order element), and FB8 (nonlinear system).The mathematical models used in this study arepresented in the results section. Finally, the stepresponse data is sent to the function FC1.

Step 3. Identification of the FOPTD model – basedon the well-known two-point method, the functionFC1 determines the plant gain k, the overall timeconstant T , and the time delay T0. Then, theobtained parameters are sent to another functionFC8 and to the HMI application.

Step 4. Controller parameterization – the functionFC8 is responsible for the controller tuning. First,based on the FOPTD model, the parameters of thePI controller are determined. Then, the obtainedparameters are used to calculate the controller pa-rameters for the DMC and B-BAC algorithms ac-cording to the methods presented in (Laszczyk andCzeczot, 2012; Laszczyk et al., 2013; Stebel et al.,2014). As a result, if there is a new set-point forthe plant, the controller parameters can be easilydetermined by using the new parameters of theFOPTD model.

Step 5. Test simulations – once the controller param-eters are determined, the function FC7 performstest simulations of the control system for the plantdescribed by the FOPTD model (identified in Step3) and for each of the controllers. Although thesetests are performed sequentially, the total timeneeded to perform all simulations is less than a fewminutes, provided that the simulations are basedon the FOPTD model of the controlled plant,

which is independent on the plant complexity. Ingeneral, the simulation time will be dependent onthe time step size. In turn, the size of the timestep is dependent on the overall time constant Tin the FOPTD model. In effect, the metamorphiccontroller can mimic the parallel design process.

Step 6. Selection of the optimal controller — the re-sults of the test simulations are used in functionFC3 to calculate the selected performance indexfor each of the potential controllers. The per-formance indices are calculated in function blocksFB10 (ITAE), FB11 (ISE) and FB12 (IAE). Theobtained results are also sent to the HMI applica-tion. The function block FB31 selects the controlalgorithm associated with the smallest value of thegiven performance index.

Step 7. Calculation of the maximum control errorEmax – for each potential controller, the functionFC4 calculates the Emax index, which is a max-imum overshot in the control system. The Emax

value is then sent to the HMI application for in-formation purposes only.

Step 8. Implementation of the optimal controller – thefunction FC5 calls one of the function blocks thatcorresponds to the selected controller: PI (FB21),DMC (FB22) and B-BAC (FB23).

Step 9. The end of the controller selection procedure– the selected controller is confirmed by the con-trol engineer and connected to the plant. Afterapproval of the new controller structure, the sys-

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tem switches to the new controller in a bumplessmanner.

Figure 6: Program structure in the PLC.

If the controller selection procedure is based on themathematical model of the plant implemented in thefunction FC2, then the whole procedure may take upto a few minutes to complete. It depends on the timestep size required for numerical stability and accuracy.Otherwise, the time required to complete the procedureis dependent on the plant dynamics, i.e., the settlingtime after a step change in the CV.

5 Evaluation results

The goal of this section is to show that by changingthe control algorithm in the metamorphic controller, itis possible to obtain a better control quality. The ef-fectiveness of the metamorphic controller was assessedbased on the simulation approach. Mathematical mod-els of the controlled plants were implemented in themetamorphic controller by means of the function FC2and numerically integrated using Euler's method witha time step ∆t=100[ms], which is achieved by the cyclicinterrupts in the controller. The cycle time of 100[ms]was sufficient to perform the necessary calculations.For comparison reasons, the tests were performed forlinear and nonlinear plants for two chosen operatingpoints of the control system. The models used are rep-resentatives of continuous processes that are commonlyencountered in industry for a chosen operating point.The linear plants, presented in the form of transfer

functions, and their FOPTD models have been shownin Table 1.

In the case of linear plants, the FOPTD mod-els (transfer functions with lower index ’M’) are thesame, irrespective of the operating point of the sys-tem. The last case presents a nonlinear model of thehydraulic system, i.e., a conical tank with a varyingcross-sectional area, and its mathematical model is asfollows:

A1h = Fin − c1√h for h < h0 (3)

A2(h)h = Fin − c2√h + h0 for h > h0 (4)

where: h - liquid level in the tank (h - time derivativeof h), Fin - input flow rate, c1, c2, A1, A2(h), ho - modelparameters.

The liquid level h in the tank, which is the processvariable (PV), and the input flow rate Fin, which is thecontrol variable (CV), determine the operating pointof the controlled plant. For comparison reasons, twooperating points of the nonlinear system (3)-(4) werechosen. Hence, the transfer functions KM4a(s) andKM4b(s) are the linear approximations of the nonlin-ear plant (3)-(4) at the chosen operating points. De-pending on the operating point, the parameters of theFOPTD models can significantly vary:

KM4a(s) =1.25

3.75s + 1(5)

KM4b(s) =3e−4.55s

8.25s + 1(6)

Table 1 shows the obtained results for each perfor-mance index and for each candidate control algorithm.

The optimal controller structures, determined by themetamorphic controller, were marked by bold numbers.In each case, the whole procedure took up to four min-utes. Figure 7 presents exemplary step responses ofthe control system around two operating points of theplant K3(s) for each potential control algorithm. Forinstance, if the new operating point of the system hasto be reached quickly with small overshoots (ITAE in-dex), the DMC algorithm should be used (Figure 7).As can be clearly noticed (Table 1), depending on thechosen performance index and operating point of theplant, the advanced control algorithms allow to achievemuch better performance than the classical PI con-troller, which is often the only option in other support-ing applications. For instance, in the case of the non-linear plant (3)-(4), the PI controller was the optimalsolution only for a the second operating point and the

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Table 1: Performance index values for two different operating points of the plant.

The first operating point The second operating pointPlant/FOPTD Controller ITAE ISE IAE ITAE ISE IAE

K1(s) = 5(4s+1)(10s+1)

KM1(s) = 5e−2.95s

11.55s+1

PI∗

DMCB-BAC

2057.03932.93523.7

4831.43526.12700.0

256.8402.4277.5

1707.54199.32337.6

4912.64951.32430.5

246.8415.6266.2

K2(s) = 3(s+1)(18s+1)

KM2(s) = 3e−0.95s

17.85s+1

PI∗

DMCB-BAC

888.56823.21139.7

2430.21094.3900.0

150.9498.2167.6

694.46752.91223.4

3036.13081.6900.1

157.6496.6189.3

K3(s) = 3e−2s

(4s+1)(6s+1)

KM3(s) = 3e−4.55s

8.25s+1

PI∗

DMCB-BAC

3676.33076.86997.9

6213.76435.34140.0

322.4358.7416.0

3856.23276.87896.9

6101.66975.34066.1

322.4376.7396.1

Nonlinear systemPI∗

DMCB-BAC

342.1300.02209.6

1907.13378.790.0

108.8104.7256.4

391.1344.32311.2

1915.13471.697.0

117.8119.7266.4

∗A possible solution that can also be obtained by other supporting tools, which do not include the advanced control algorithms.

Figure 7: Control system responses with the plant K3(s) to step changes in the SP value: a) for the firstoperating point (SP change at 100[s]); b) for the second operating point (SP change at 450[s]).

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IAE performance index. In other cases, a much betterperformance was obtained for more advanced controlalgorithms, included in the metamorphic controller.

This justifies a modification of the controller struc-ture at the controller design stage and during normaloperation of the system. Owing to the metamorphiccontroller, the change of the control algorithm and theapplication of more advanced controllers can be carriedout without the help of cooperating experts.

6 Concluding remarks

In this work, the metamorphic controller with extendedfunctionality has been presented. The obtained resultsshowed the possibility of using the metamorphic con-troller as a tool which supports control engineers at thecontroller design stage. Because the presented tool usesthe mathematical model of the plant, the candidatecontrol algorithms can be tested through simulationsin less than a few minutes, thereby shortening designtime and overall costs. In turn, during normal oper-ation of the plant, the metamorphic controller can beused to select an optimal control algorithm for a givenoperating point of the plant with respect to technolog-ical constraints. Since, the tuning procedures use theFOPTD model (1), the metamorphic controller can beused for each plant that can be described by the model(1). Moreover, in comparison to existing tools and sup-porting applications, the metamorphic controller al-lows using more advanced control algorithms withoutthe need of employing an expert or several cooperatingexperts which can be an expensive option. In this case,the role of the user (control engineer) is limited to theselection of the operating point and the performanceindex that determines the behavior of the control sys-tem. As a result, less experienced engineers are ableto use more advanced controllers. A drawback of theapproach is that the tuning methods use parametersof the linear model of the plant. Hence, it may turnout that an expert (or a group of experts) is able toprovide better tuning parameters, i.e., the controllerthat ensures a better performance of the closed-loopsystem. This is the price we pay for replacing the ex-pert with the metamorphic controller. As shown bythe experimental results with the use of the typicalindustrial PLC and simulated plants, a change of thecontrol algorithm may lead to a better performance ofthe control system.

The metamorphic controller can be further devel-oped in many different directions, including implemen-tation of more optional control algorithms. Compli-ance with the IEC 61499 standard simplifies imple-mentation of the metamorphic controller in the dis-tributed control system using various hardware plat-

forms, but the versatility of the system makes it appli-cable in any time-determined networking environment(Polakow and Metzger, 2013).

Acknowledgments

This research work was performed within scientific co-operation between Sogn og Fjordane University Col-lege and Silesian University of Technology and is thecontinuation of two joint research projects that weresupported by Polish-Norwegian Research Fund: Geol-ogy and Information Technology (GIT - PNRF 164-AII-1/07 2008-2009) and Automated Assessment ofJoint Synovitis Activity from Medical Ultrasound andPower Doppler Examinations using Image Process-ing and Machine Learning Methods (MEDUSA Pol-Nor/204256/16/2013 2016). The work was also par-tially supported by the Ministry of Science and HigherEducation under grants BK-UiUA and BKM-UiUA.

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