real-time integrated process supervision

Engineering Applications of Artificial Intelligence 13 (2000) 645–658

Real-time integrated process supervision

C. Quek*, A. Wahab

Intelligent Systems Laboratory, Nanyang Technological University, Nanyang Avenue, Singapore 637989

Abstract

This paper presents the use of a micro-controller-based integrated process supervision (IPS) system as a real-time platform forinvestigative work in structuring expert control. Two different control approaches, based on classical and artificial intelligencetechniques, were integrated within IPS and serve as practical examples of the structured approach to expert control. The IPS is a

refinement of the expert control architecture. It allows the integration of several control techniques in a single generic framework.Specifically, the paper presents the extensive experimental results derived from a micro-controller-based implementation of IPS onthe real-time control of a typical industrial heat-exchanger process. The classical approach, based on auto-tuning techniques, was

implemented under the IPS framework. Three auto-tuning techniques, namely Ziegler–Nichols tuning, amplitude tuning and phasetuning were incorporated. In addition, neural-network-based control techniques using the modified cerebellar model articulationcontroller (MCMAC) were also seamlessly incorporated within the IPS scheme. The real-time experimental results using the IPS

architecture significantly demonstrated the effectiveness of IPS in handling varying operating conditions. Furthermore, the inclusionof both AI and classical control techniques within a common supervisory framework adequately shows the generality of thearchitecture. # 2000 Elsevier Science Ltd. All rights reserved.

Keywords: Real-time integrated process supervision; Micro-controller-based IPS; Noise tolerance; Neural network control; Auto tuning; Generic

control regimes; Performance degradations; Modified cerebellar model articulation controller

1. Introduction

It is widely accepted that for a control system, it isinadequate to have a single set of controller parametersthat would allow the system to perform satisfactorilythroughout the life span of the system. This may be dueto various reasons such as the controlled equipmentundergoing wear and tear to other external disturbancesthat changes the operating conditions of the controlledprocess. In trying to accommodate the changes that acontrolled process is expected to undergo, controlsystems are often complicated as they contain a mixtureof algorithms based on theories as well as heuristics.

The expert control (EC) architecture (Arzen, 1989;Astrom and Arzen, 1992; Astrom, 1991) was asignificant step in separating control algorithms fromheuristics that represent human-imposed limits. All thealgorithms within the EC architecture are co-ordinatedby a knowledge base. It fulfils the condition of beingsufficiently generic to allow the use of certain classes of

algorithms where appropriate. However, there are noguidelines available within the architecture to organisethe various rules and procedures. The knowledge basehas to supervise and undertake various tasks such astuning, model identification etc., and hence the systembecomes very complex, and is implemented on an ad hocbasis.

2. Integrated process supervision (IPS)

Integrated process supervision (IPS) attempts toreduce the complexity involved in the EC architectureby imposing a clear separation between supervision andtask-specific control functions such as regulatory controlor adaptation. This separation is the main deviation ofIPS from the EC architecture (Quek et al., 1995). TheIPS (Leitch and Quek, 1992) attempts to address theinadequacies in expert control. It provides a generalarchitecture where different control functions can beintegrated within a single system so that appropriatecontrol function may be activated whenever necessary.

*Corresponding author.

E-mail address: [email protected] (C. Quek).

0952-1976/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved.

PII: S 0 9 5 2 - 1 9 7 6 ( 0 0 ) 0 0 0 5 2 - X

2.1. Basic concepts

The continuous monitoring of system performanceallows the selection and application of appropriatecontrol techniques to drive the system to meet pre-setperformance objectives. Supervision in any architectureshould possess knowledge of:

* the system performance objectives,* the process characteristics and behaviour, and* the capabilities and limitations of the control al-

gorithms being used.

Thus, the supervisory module must be able to constantlymonitor the plant, apply appropriate control techniques,and reassess the system performance, thereby, extendingthe operational range of the control system. Moreimportantly, it should be able to react appropriately inemergency and critical situations.

In IPS, the techniques used for process control can bebroadly grouped under primary control, adaptivecontrol and fault analysis regimes. This classificationallows a supervisory framework to be formulated asshown in Fig. 1. Activation of different control regimesby the process supervisor is specified through the use ofa performance index or a combination of performanceindices. The supervisory module monitors the selectedtechnique by assessing the controller’s ability to performits intended function. This minimises the interactionbetween the tasks of supervision and control. It enablesIPS to achieve generality by allowing any controltechnique to be applied for a task-specific controlfunction (e.g., regulation or adaptation).

2.2. Behavioural classification

Behavioural classification, based on the performanceof the process, allows the association of control regimeswith different performance groupings. IPS utilisessupervisory inputs such as a performance index (e.g.,one based on the closed-loop error) to classify systembehaviours into three classes. When the performance ofthe system meets a specified requirement, the system isconsidered to exhibit acceptable behaviour. Primarycontrol functions such as proportional-integral-differ-ential (PID) control will be scheduled when acceptablebehaviour is determined. On the other hand, if the

performance of the system deviates from the specifiedrequirements, the system is considered to be exhibitingmalfunction behaviour. This may be due to wear andtear of the system, or to the degradation of systemparameters. The process supervisor schedules techniquesunder an adaptive control function to tune the primarycontroller to produce a better performance. Drastic orfurther degradation may force the adaptive control tofail; in this case, the system will be classified asexhibiting faulty behaviour.

In IPS (Leitch and Quek, 1991), a crisp-set-basedsupervisory module handles behavioural classificationby comparing the system performance against twothreshold values, X% and Y%, also known as themalfunction and fault boundaries, respectively, asshown in Fig. 2. These are user-defined design par-ameters in IPS, and are usually specified as percentagevalues of a reference cost function. The supervisorymodule constantly evaluates the system performanceagainst these boundaries to determine the systembehaviour class. The system exhibits acceptable beha-viour when its performance is less than X%. BetweenX% and Y% the system is said to exhibit malfunctionbehaviour, and once the system performance degradesbeyond Y%, the supervisory module would indicatefaulty behaviour. Additional work to investigate thefuzzification of the behaviour classification is currentlybeing actively undertaken. A separate paper presenting aformal analysis of the fuzzification process and theperformance of the integrated fuzzy process-supervisionscheme has been submitted for journal review (Queket al. 2000).

2.3. Control regimes

A faulty behaviour implies that no controller is ableto bring the process back to a state in which theperformance will be within the specified level. However,if the current controller is unable to bring the process toa desired state but other controllers within the IPSframework are able to do so, then the behaviour

Fig. 1. IPS architecture. Fig. 2. Behaviour classification.

C. Quek, A. Wahab / Engineering Applications of Artificial Intelligence 13 (2000) 645–658646

exhibited by the process is seen as malfunctioning. Thesecontrollers are designed on-line through an appropriateadaptive control technique.

Under the initial primary control regime the con-troller is designed using a model of the process, andassumes a set of a priori operating conditions. Thecontroller is effective when there are only smalldeviations in the parameter values and no physicalstructural changes to the system. Primary controllerscan be of the following types: PID controllers, linguisticfuzzy controllers (Mamdani, 1976; Albertos et al., 1993)or neural controllers (Quek and Ng, 1996a).

Once the assumptions supporting the choice of thevalues of the parameters of the primary controller are nolonger valid, the adaptive control regime is activated.The adaptive control algorithms are assumed to be ableto update the controller parameters on-line in sucha way that the new values/settings will allow theredesigned controller to meet the specified performance.Examples of adaptive controllers include the modelreference adaptive scheme (MRAS) (Landau, 1979;Quek and Leitch, 1993), the modified cerebellar modelarticulation controller (MCMAC) proposed by Quekand Ng (1996a), auto-tuning techniques and self-tuningregulator (Astrom and Wittenmark, 1980; Astrom andHugglund, 1983).

The fault-diagnostic regime is activated when nocontrollers; either primary or adaptive, are able to bringthe system back to the specified performance level. Sincethe system is exhibiting faulty behaviour, the primarytask of the diagnostic system is to determine the changesthat have occurred in the structure of the system, and toprevent it from escalating to an unsafe state. Im-plementation of this regime is, however, not within thescope of this paper.

2.4. Supervisory control

The supervisory module is a key component in IPS. Itis solely responsible for the pattern classification ofsystem behaviours, as well as the activation of suitablecontrol regimes on the basis of this classification toachieve the desired performance specification. Animportant aspect of IPS is the selection of an appro-priate set of supervisory inputs, from which the systembehaviour class can be determined. Supervisory inputsin IPS can be a combination of performance criteriainvolving closed-loop error, control effort and modelerror. These inputs allow the supervisory module todetect transitions in behaviour.

2.4.1. Realisation of IPSA typical realisation of IPS is shown in Fig. 3. The

objective of the architecture is to ensure that the controlsystem produces the required performance by activatingthe appropriate regime, on the basis of the behaviour

classification. Initial boundaries are often set prior tocommissioning the system, and are then adjusted oncethe system is in operation. Additional exploratoryresearch is currently being undertaken to examine thefuzzification of the behaviour-classification schemeusing fuzzy-rule-base techniques as well as hybrid fuzzyneural-network techniques (Zhou and Quek, 1996; Quekand Zhou, 1999) and their performance under Type Iand Type II classification errors. The latter are errormeasurements used for benchmarking pattern-classifica-tion systems (Jay, 1988). The behaviour-classificationscheme used in IPS can be construed as a controlpattern-classification problem, and supervisory systemsusing such techniques to categorise and activate controltechniques can then be objectively evaluated.

3. Process rig auto tuning

A real-time micro-controller based IPS frameworkwas implemented using a laboratory-scale industrial-type heat exchanger, which can be configured in theexperiments for flow/temperature control. The choice ofsuch a hardware platform to validate the IPS frameworkis intentional. The thermal process rig is typical of manynon-linear industrial process, albeit on a reduced scaleso that it is manageable within a laboratory environ-ment. The physical processes are similar to ‘‘realplants’’, and hence the results should provide represen-tative indications as to the performance of the IPSframework.

3.1. Process rig hardware

Fig. 4 shows the process rig, with nine major physicalcomponents of the heat exchanger.

The injection of malfunctions into the system isgreatly simplified by controlling the rate of flow, thediverter solenoid, the drain solenoid, the fan behind theradiator, the blockage valve to simulate blockages in thepipe, the heater gain constant and the cooler gainconstant.

Fig. 3. Realisation of IPS.

C. Quek, A. Wahab / Engineering Applications of Artificial Intelligence 13 (2000) 645–658 647

The most common method of auto tuning is a simpleopen-loop or closed-loop test (Ziegler and Nichols,1942; Cohen and Coon, 1953). In the open-loopexperiment, the transient output response of the processis observed after a pulse or a couple of step changes haveexcited the process. Parameters obtained from the open-loop step response are used to estimate the PID values,and the tuning rule was developed on the basis ofempirical simulations.

An alternative method of auto tuning uses theproportional controller with feedback. The gain of thecontroller is increased until the output of the systembegins to oscillate with steady amplitude. A majordrawback in these methods is that the transient responseis highly sensitive to disturbances. In the experiment theZiegler–Nichols closed-loop technique was used asproposed by Astrom (1983) to minimise this problem,see Fig. 5. The parameters of the PID controller weredetermined using the critical gain and period method.

3.2. Experimental set-up

Experimentation with the various auto-tuningtechniques was organised under two groupings. Thefirst set of experiments shows how IPS determines the

performance and schedules the auto-tuning technique toredesign the PID settings as the operating region ischanged. The second set of experiments examines theaction of the supervisory module in handling malfunc-tions in the system.

The Bytronic heat exchanger was set up throughoutthe experiment with the pump set at 30%, the divertervalve set at 100%, the cooler set at 100%, the stirrer setat ON and the drainage valve set at OFF. For both setsof experiments, the supervision module is turned ONwhen appropriate. The amplitude of the relay tuner wasadjusted according to the reference temperature. Fortemperatures below 508C, the amplitude of the relay wasset to 12% of the maximum heater effort, while fortemperatures above 508C, the process was tuned withthe relay tuner amplitude set to 40% of the maximumheater effort.

The reference model performance in the two sets ofexperiments was determined by monitoring the systemperformance under the influence of a toggling effect.This was performed prior to the experiments. The costfunction for this toggling period was computed and usedas the reference performance. This is also known as thereference cost function or Jref , and is calculated using theintegrated square error (ISE) formula shown in Eq. (1):

Jref ¼Z tn

t0

plant errorð Þ2 dt ð1Þ

where t0 and tn are the start and end times of the toggle,respectively. The performance of the system during theexperiments is normalised against the reference per-formance and is described in Eq. (2). The reference costfunction Jref is computed prior to the start of theexperiments during the start up period.

NISE ¼R tnt0

plant errorð Þ2 dtR tnt0

model errorð Þ2 dtð2Þ

where ISE is the integrated square error and ½t0; tn� is theinterval of error computation.

3.3. Experimental results

This section examines the experimental results withvarious auto-tuning techniques, under the close super-vision of the IPS. In addition, it examines the effect-iveness of IPS in handling exogenous parameterdegradation using the auto-tuning PID set-up.

3.3.1. Auto tuning under IPSThis set of experiments attempts to demonstrate the

real-time scheduling of various auto-tuners under theintegrated process supervision scheme. The processsupervisor switches the control techniques fromthe primary control regime; i.e. PID control, to theadaptive control regime; i.e. auto tuning, whenever the

Fig. 4. Process rig.

Fig. 5. Implementation of auto tuning in IPS.


performance is assessed as being unsatisfactory. Thereference performance is determined by monitoring thesystem performance under the influence of a togglingeffect. The cost function during the toggling period iscomputed and used as the basis to determine the‘‘satisfactory’’ performance value for Jref. In theseexperiments X% and Y% as percentages of Jref wereset at 120 and 240%, respectively.

Toggling was introduced once the parameter tuninghad been completed. This was necessary in order toallow the process supervisor to re-calibrate the referenceperformance using the new controller parameters.Calibration was done with a 5% toggle about thecurrent reference temperature setting. The time for eachtoggle was set at four minutes, which was roughlyequivalent to three times the critical period obtainedduring the tuning phase. Each of the tuners that wereimplemented was observed under three major tempera-ture changes. The reference temperature level was set at40, 50 or 608C, in no particular order. In addition, smallreference changes were also introduced. Differentorderings of the major reference changes were used soas to observe both the heating and cooling effects of thesystem and the reactions of the process supervisor.

The results from the experiments are shown in Figs.6–8. Fig. 6 shows the results using Ziegler–Nicholstuning, while Fig. 7 shows the results under amplitudetuning. Results for phase tuning are presented in Fig. 8.In each of the figures provided, the graphs show theprocess temperature and system performance (expressedin NISE values) from the experimental runs. The NISE

index was calculated using Eq. (4). For all the graphsshown, each interval along the time axis represents asample of 2 s. The instant Ta shows the time at which theIPS invoked the adaptive control regime. Tc representsthe time at which auto tuning of the PID controller wascompleted. This also represents the beginning of thecalibration phase for the new operating condition. Attime Tn, the PID controller was again fully operational.

(a) Ziegler–Nichols tuning under IPS. This experimentdemonstrates the Ziegler–Nicols auto tuning under thescheduled control of the IPS. The behaviour of theprocess, shown in Fig. 6, was observed with a stepchange of the reference temperature from 40 to 508C.The system was tuned and calibrated (prior to time Ta1)at a level within the malfunction boundary, X%. At timeTa1, the reference temperature changed from 40 to 508C,forcing the cost function to rise above the malfunctionboundary, X%. The supervisory module detected thisoccurrence and immediately switched over to the relaytuner for auto tuning. From time Ta1 to Tc1, relay tuningwas applied, and the new PID values were subsequentlyupdated. At time Tc1, when stable limit cycles had beenachieved, the relay tuner was deactivated. New par-ameters of the PID controller were adjusted using thecritical period and amplitude derived from the stablelimit cycles. After Tc1, toggles were injected into thesystem to allow calibration of the reference costfunction, Jref , by the process supervisor.

Observations of the NISE value during calibrationshowed that the cooling effect in the system was muchbetter than the heating effect, as the controller was able

Fig. 6. Ziegler–Nichols tuning under IPS.


to reduce the process temperature to a lower setting in amuch shorter time. Preliminary results indicated that theheating and cooling effects were widely different atdifferent temperature settings. Thus, different NISE

values were observed during the calibration period.Under this situation, the NISE values for thelower toggles were consistently lower than thoseof the higher reference toggles. The reference cost

Fig. 7. Amplitude tuning under IPS.

Fig. 8. Phase tuning under IPS.


function was selected on the basis of the higher of thetwo. This in effect lowers the sensitivity of the schedulingmechanism. Generally, the heating effect is moreprominent at lower temperatures, while the coolingeffect performs better at higher temperatures.

The results demonstrate successful automatic tuningof the system using the Ziegler–Nichols method underthe direction of the process supervisor. The processsupervisor was able to correctly determine the systembehaviour, and to schedule the appropriate controller toimprove on the system performance as the operationalconditions changed.

(b) Amplitude tuning under IPS. This experimentshows the scheduling of the amplitude tuner by IPS. Thereference temperature setting was adjusted from 50 to408C. The sequence serves to investigate the coolingperformance of the system under the controlled schedul-ing of the amplitude tuner by the process supervisor.

The system performance is expressed in terms ofNISE values throughout the experiment as shown inFig. 7. A 5% temperature change during the calibrationperiod allows the process supervisor to take intoaccount both the heating and cooling performance inthe system. The value of the system supervisory costfunction increased beyond X% when the referencetemperature was decreased from 50 to 408C. Asexpected, at time Ta1, a switch to the adaptive controlregime was made when the process supervisor classifiedthe behaviour as a malfunction. The PID values werethen adjusted for the current reference temperature atTc1: Limit cycles occurred in the process temperaturefrom time Ta1 to Tc1 when the system underwent autotuning. Re-calibration of the cost function was per-formed between Tc1 and Tn1. The calibration of the costfunction was accomplished by taking the maximumsystem cost function observed for each toggle. Thenormalised cost functions for the upper and lower toggleare similar. By time Tn1, the system was operating innormal mode.

The system behaviour was also investigated for asmaller change in temperature. A 58C change caused theIPS to auto-tune again at time Ta2. This is due to thefact that the 58C change was well above the 5% changeemployed in calibrating the reference cost function.Moreover, the cooling effect was substantially lesseffective at lower temperatures.

The amplitude tuning was able to re-design theprimary controller to adapt to both large and smallchanges in the system operation under the close super-vision of the IPS. In addition, the behaviour classifica-tion and appropriate scheduling of control regimes weresuccessfully achieved, for both temperature incrementsand reductions.

(c) Phase tuning under IPS. This experiment investi-gates the effect of tuning the system using the phasetuner under the IPS architecture. The temperature was

varied from 40 to 508C. The experimental results arepresented in Fig. 8. The process temperature and NISEvalue collected during the experiment were plottedagainst time at 2 s intervals. X and Y% were again setat 120 and 240% of Jref, respectively.

When the reference temperature was increased from40 to 50oC, a significant degradation in system per-formance caused the IPS architecture, at time Ta1, toswitch to the adaptive regime. After tuning, the PIDcontroller was able to meet both rise time and overshootrequirements satisfactorily, as shown in Fig. 8. Thesystem switched back to the primary control regimeafter the time instant Tn1 when the values were adjustedand reference performance calculated.

From the three sets of experiments, the processsupervisory module successfully demonstrated the real-time integration of the various auto-tuning techniquesunder the IPS architecture. Though a simple crisp setsupervisor was utilised here, it was shown that theclassification of system behaviour was still achievable,and that this assisted in enhancing the performance ofthe control system.

3.3.2. Integrated process supervision under systemdegradation

The second group of experiments examines theresponse of the process supervisor when the processundergoes changes in the system parameters. Theseparametric changes are simulated through the reductionof the gain constant, Kh, of the heater element. Thetemperature of the process was initially maintained at408C. When the system stabilised, a degradation of theheater element was introduced and the behaviour of thesystem observed. The heater control effort directlyinfluences the amount of heating in the system, andcan be obtained by multiplying the control output andthe heater gain constant. The degradation of system’sperformance was introduced by reducing the heater gainconstant to 80% at time TD.

Fig. 9 shows the results under IPS supervision withthe Ziegler–Nichols tuner as the adaptive technique. Thesupervisory module was able to detect this occurrence.From time TD to Ta, the cost function increased as thePID controller was unable to response adequately tothis degradation. At time Ta, the cost function had risenbeyond X%. The process supervision subsequentlyactivated the relay tuner in an attempt to improve theperformance. The tuning improvement can be observedfrom the value of the cost function saturating withinX%. From time Ta to Tc, a stable limit cycle wasachieved using the relay tuner. At time Tc, the PIDparameters were accordingly adjusted. Similar to pre-vious experiments, the duration from Tc and Tn wasused to calibrate the reference cost function, Jref , bytoggling the reference temperature about 5%. At timeTn the newly adjusted PID controller was now able to


maintain the process temperature at the referencesetting. For auto-tuners using both amplitude and phasetuning, the performance of the system was similar tothose derived using the Ziegler–Nichols tuner. Theresults derived in this experiment were similar to theprevious set. The process supervisor was able to detectthe malfunction in the system and switch to the adaptivemode.

These experiments successfully demonstrated thescheduling of the auto-tuner as the system underwentvarying operating conditions under the close guidance ofthe integrated process supervisor. Various auto-tuningtechniques were seamlessly incorporated within the IPSarchitecture. The supervisory module was also able todetect the degradation, and switched between control-lers as necessary.

4. Modified cerebellar model articulation controller

The modified cerebellar model articulationcontroller (MCMAC) proposed by Quek and Ng(1996a) is based on the cerebellar model articulationcontroller (CMAC) (Kraft and Campagna, 1990).CMAC requires a classical controller to be used duringthe learning phase. This makes it difficult and complexto incorporate. The MCMAC eliminates the use of aclassical controller.

4.1. Cerebellar model articulation controller

In most neural-network systems, the learning phase isgenerally slow and is performed off-line. Such neural-network systems are unsuitable for incorporation withinthe IPS. The IPS requires on-line training of the primarycontroller, and adaptation to new parameters to takeplace within a relatively short period of time. A CMACis essentially an associative memory that partitions theinput space, and is a suitable candidate for use withinthe IPS scheme. It can be rapidly trained without theneed to search for a global minimum, and in essence,uses ‘‘pre-programmed’’ adaptive control in its learningphase to produce a gain-scheduled system. The CMACmodifies its control effort in response to changes inprocess measurements, and can be trained on-line.However, during the encoding or learning phase, theCMAC requires the presence of a classical controller,e.g. a PID controller.

The basic structure of the CMAC is very similar to thePerceptron (Rosenblatt, 1961), and it is fundamentally alook-up table where the basic functions are generalisedlocally (Brown and Harris, 1994). It has been used formodeling and controlling high dimensional, non-linearplants such as robotic manipulators (Commuri et al.,1997; Lin and Song, 1998; Miller et al., 1990). Manydifferent schemes have also been proposed to improvethe basic algorithm (Ker et al., 1997; Quek and Ng1996a; Brown and Harris, 1994; Lane et al., 1992).

Fig. 9. System degradation with process supervision using Ziegler–Nichols tuning.


CMACs have been proposed for closed-loop control ofcomplex dynamic systems (Commuri et al., 1997; Linand Song, 1997), and learning convergence of a CMACwas established in Lin and Chiang (1997) and Wong andSideris (1992).

The basic idea behind the CMAC is to learn anapproximation of the system characteristics in the formof a gain-schedule, and subsequently to use it togenerate an appropriate control signal. This approxima-tion is established on the basis of the real-timeobservations of the plant input and output data. Theschedule is stored in an associative memory. Theapproximation of the schedule for the process rig isderived from observation of the desired output and theactual target response in the rig. CMAC computes theadjustments to the weights in the associative cells usingthe error signal generated from the difference betweenthe two signals. Fig. 10 shows the CMAC set up for theneural control of the process rig.

The associative memory of the CMAC is representedas a two-dimensional array. The indices to this array areyðtÞ and yðt21Þ; where the former denotes the output ofthe process at time t and the latter represents the outputat time t21. The content of the array at any locationrepresents the input to the process. When the CMAC isin normal operation, the inputs to the controller are thecurrent process output and the desired output. UsingyðtÞ as the desired output and yðt21Þ as the currentoutput, the content retrieved from the associative mapwðtÞ is used as the new input to the plant.

In the CMAC learning phase, the control law isderived from a classical controller. An error-minimisa-tion process known as the Delta learning rule isemployed. The CMAC keeps track of the actual outputof the process at time t and the previous output at timet21. In addition, it monitors the process input, x(t),which is the sum of the classical output and the CMACoutput. The neural network attempts to shift the value

at location (yðtÞ, yðt21Þ) towards xðtÞ by a certainfactor. Eq. (4.1) describes the Delta learning rule.

w y tð Þ þ i½ � y tÿ 1ð Þ½ � ¼ a x tð Þ ÿ w y tð Þ½ � y tÿ 1ð Þ½ �ð Þ ð3Þ

where t is the time instant, xðtÞ the process input at timet, yðtÞ the desired output, yðtÿ 1Þ the current output,and a is the learning constant.

The Delta learning rule guarantees the steepestdescent to the minimum error and, as a result, it has afast learning speed. Although its simplicity, fast learningspeed and on-line training mode make it an appropriatecandidate to be incorporated into IPS, the learningphase in the CMAC is difficult to plan since the learningphase follows a path dictated by yðtÞ and yðt21Þ.In such cases not all cells in the associative memoryare updated during learning. Furthermore, theCMAC requires a classical controller as a model duringtraining.

4.2. Modified cerebellar model articulation controller

The major difference between the MCMAC and theCMAC is the need for a classical controller. Theassociative memory of the MCMAC is implemented asa two-dimensional array using the desired output andthe plant closed-loop error as the array indices.Retrieval of the cell content is identical to that in aCMAC. However, instead of using the actual processoutputs yðtÞ and yðt21Þ, the MCMAC uses the closed-loop error of the system, eðtÞ, as well as the actualoutput, yðtÞ, as the indices to access any cell content.This is done for every sample taken.

During the MCMAC learning phase, the output ofthe process and its current closed-loop error are used toretrieve the cell content as the input to the system. Aftera sufficient time lag, t, the closed-loop error is thennoted. The cell at which this input has been derived is

Fig. 10. Cerebellar model articulation controller.


then shifted according to the sign and magnitude of theerror. The modified learning rule is given in Eq. (4).

w y tð Þ½ � e tð Þ½ � ¼ ae tþ tð Þ ð4Þ

where t is the time instant, yðtÞ the control effort thatresults in the closed-loop error at time t, eðtÞ the closed-loop error at time t, a the learning constant, and t is thetime lag required.

Using the above learning rule, the greater the closed-loop error, the more positive the control effort becomes.The MCMAC training may be difficult to plan. This isdue to the fact that the learning in the MCMAC followsa path dictated by yðtÞ and eðtÞ. Cases may still occurwhere some of the cells in the associative memoryremain untrained at the end of the learning phase.

4.2.1. Local generalisationTo overcome the problem inherent in the planning of

the training sequence, local generalisation can be used toensure that neighbouring cells are updated during learning.Under this scheme, a group of cells are simultaneouslyupdated instead of only one cell. This is based on theassumption that neighbouring cells recall similar processbehaviour. Thus, a learning rule that incorporatesneighbourhood learning is proposed. The Delta learningrule using local generalisation is given in Eqs. (5) and (6).

w y tð Þ þ i½ � e tð Þ þ j½ �¼ a x tð Þ ÿ w y tð Þ þ i½ � e tð Þ þ j½ �ð Þ

ð5Þ

x tð Þ ¼Xci¼ÿc

�Xcj¼ÿc

w yr þ i½ � y tð Þ þ j½ �2cþ 1ð Þ2

where i; j 2 ÿc; c½ �

ð6Þwhere t is the time instant, xðtÞ the input to thecontrolled element at time t, yðtÞ the control effort thatresults in the closed-loop error observed at time t, e(t) isthe closed-loop error at time t, i and j are the indiceswithin the size of square of cell to be updated, and crepresents the size of the square of cells to be updated.

4.3. Experimental set-up

The experimental work on the MCMAC was or-ganised into two groups of tests; namely:

* the assessment of the performance of the MCMACagainst PID control under auto tuning, and

* the performance of the MCMAC with systemdegradation under the guidance of the IPS scheme.

The components in the process rig were set up with thepump set at 30%, the diverter valve set at 100%, the

cooler set at 100%, the stirrer set at ON and theDrainage valve set at OFF. The supervisory module wasonly switched ON during the last set of experiments.The MCMAC was trained using a toggle of 108C aboutthe reference setting.

4.3.1. MCMAC vs. PID control with auto tuningThese experiments attempt to assess the performance

of the MCMAC against the PID controllers that havealready been auto-tuned. The performance of bothcontrollers in response to step inputs was measured andanalysed. The overshoots and rise times of the stepresponses were recorded at three reference temperaturesettings: 35, 45 and 558C. Five experiments wereconducted at each reference temperature, using boththe PID and MCMAC controllers. The PID controllersused in the experiments were auto-tuned prior to thestep changes. Each change is a toggle of 5% about thereference temperature.

The MCMAC controller was trained prior to theexperiments. Each cell in the associative memory ofMCMAC was initialised with a control effort corre-sponding to 8% of the maximum value. The MCMACwas trained using a 58C toggle about the referencesetting. Five sets of toggles were used. When theMCMAC control was commissioned, its response tothe same sequence of input changes was recorded.Fig. 11 shows the graph of overshoot vs. rise time for thetwo controllers.

The MCMAC performs better in terms of rise time atvarious temperature settings. The improvement inovershoot, on the other hand, is only marginal. Ingeneral the PID controller performed better for over-shoot. At 35 and 458C settings, the MCMAC controllerprovided better responses in terms of both rise time andovershoot. A straight line drawn on Fig. 11 can be usedto separate the two groups of controllers. In general, theMCMAC has the potential to achieve better per-formance, in contrast with the auto-tuned PID counter-parts. In addition, the MCMAC networks need only tobe trained using some initial estimates of the processbefore they can become operational. This training phaseis important, as it determines the quality of thecontroller.

4.3.2. MCMAC controller with degradation in the systemThe second set of experiments investigates the

effectiveness of the MCMAC neural learning rule underthe scheduled activation by IPS. The results are similarto those produced by scheduling the auto-tuners,thereby demonstrating the effective use of neural-network techniques within the IPS framework underreal-time conditions. The process supervisor activatesthe learning mechanism of the MCMAC to allow thecontroller to adapt to the new change. This allows theneural network learning ability of MCMAC to perform


online updates of the associative memory. In this set ofexperiments, X and Y% were set at 120 and 240% of thereference cost function, respectively. The reference costfunction was computed once the MCMAC had beentrained by injecting toggles about the reference tem-perature setting prior to the experiments. The exper-iment was set up to observe the MCMAC’s ability tomaintain the process temperature at 55oC undersupervised activation by IPS when the system wassubjected to plant degradation.

The MCMAC was trained prior to the experiment.The training phase included five sets of toggles, witheach toggle lasting 4min. The degradation in the systemcomponent was simulated with a 20% decrease in theheater gain constant, Kh. Experiments conducted with-out the supervision by the IPS were performed in orderto observe the behaviour of the system under onlyMCMAC action, and are shown in Fig. 12. Fig. 13shows the results of the experiments when processsupervision is activated.

Figs. 12 and 13 show the process temperature, costfunction and heater control effort during the duration ofthe experiment. The control effort is expressed as a valuebetween 0 and 255. A control effort of 0 corresponds tothe heater under 0% PWM duty cycle, while a value of255 corresponds to a 100% PWM duty cycle. Eachinterval on the time axis represents an interval of 2 s.The time TD shown in Figs. 12 and 13 signifies the timewhen degradation is first introduced into the system. Ta

represents the time when the learning rule has beenactivated by the process supervisor. Tn is the time atwhich adaptation of MCMAC has stopped. In Fig. 12,Tm is the time at which the computed cost function hascrossed the malfunction boundary value while Tf,represents the time when the fault boundary has beenexceeded.

The degradation in the system was undetected andthe existing MCMAC controller could not cope, see,

Fig. 12. From time TD onwards, the temperature of theprocess was unable to satisfactorily regulate to therequired reference setting. At the same time, the costfunction monitored increased. At time Tm, the costfunction crossed the malfunction boundary and, furtheron, at time Tf it exceeded the fault boundary. Theprocess supervision was activated and the degradationof the heater element was introduced at time TD asshown in Fig. 13. From time TD to Ta, the cost functionof the system increased as a result of the inability of theMCMAC to meet the specified performance. TheMCMAC increased its effort after time TD to compen-sate for the growing error. At time Ta, the supervisorymodule activated the learning process in the MCMACas the system performance crossed the malfunctionboundary, X%. Online training of the MCMAC, duringthe duration (Ta;Tn), gradually updated the associativememory to take account of the parametric changes duephysical plant degradation. At time Tn, learning wasdeactivated after two sets of toggles. There was asignificant increase in the MCMAC control values fromtime Ta to Tn as shown in Fig. 13. This is producedusing neighbourhood gradient descent learning on theMCMAC associative memory, and an improved ‘‘gain-schedule’’ is subsequently identified at the end of thetraining process.

With the increased control effort from time Tn

onwards, the process temperature was maintained aboutthe reference setting. Once the network is trained andoperating in the recall mode, the control system becomesan open-loop gain scheduled control system, and theneural control schedule is only revised using theMCMAC learning rule when the system performancedegrade. MCMAC belongs to the class of CMAC neuralassociative memories. Lin and Chiang (1997) and Wongand Sideris (1992), established the convergence of suchnetworks. Hence, the use of such a novel approach byapplying MCMAC to derive the ‘‘neural gain schedule’’

Fig. 11. MCMAC vs. auto tuning.


is sound. In addition, the rate of the increase in thesystem cost function was significantly reduced, seeFig. 13. This experiment demonstrates the activationof neural-network learning under the close supervisionof the IPS to cope with changes in the system. The use ofMCMAC eliminates the need for priori informationon the system.

Further, the experiment demonstrates the effectivereal-time integration of an AI-based neural-networkcontroller within the IPS architecture. This shows thegenerality of the scheme, as a structured approachtowards expert control, whereby the supervisory rulebase is devolved from the control specific tasks, be theyAI-based or classical techniques.

5. Conclusion

The primary focus of the work demonstrates theeffective use of auto-tuning techniques as well asMCMAC neural control within a real-time IPS frame-work. In particular, an emphasis is laid on explicitlyevaluating the ability of the current control technique to

deal with parametric changes in an IPS-based systemand to suitably schedule the adaptive regime to improveon the system performance induced by physical degra-dation.

The experimental results using auto-tuners clearlydemonstrate the switching of the auto-tuners as anadaptation mechanism under the guidance of a super-visory rule base. The auto-tuners were capable ofadjusting the PID parameters in response to changesin system parameters. Under MCMAC control, theresults affirmed the ability of the neural-networklearning process to identify an improved ‘‘gain-schedule’’. In addition, the experiments also quanti-tatively substantiated the superior performance ofMCMAC neural control over an auto-tuned PIDcontroller.

The integration of MCMAC and auto-tuning tech-niques in IPS under real-time conditions has clearlydemonstrated the generality of the architecture inaccommodating different AI-based and classical controltechniques, as well as the scheduling of the primarycontrol and its associated learning algorithm. Byseparating the supervisory and control functions, the

Fig. 12. Degradation of the system using MCMAC without process supervision.


integration of traditional and AI-based control tech-niques using IPS is modular and effective. IPS, there-fore, is able to serve as an effective and structuredapproach to the use of expert control in conventionalsystems.

Efforts to investigate the fuzzification of the regime-scheduling technique by the supervisor have beentaken under at the Intelligent Systems Laboratory,NTU/SAS, and are reported separately (Quek and Ng,1996b). In addition, investigative work has also beenundertaken to explore an objective construction ofthe supervisor rule base using a Novel self-organisingfuzzy neural-network based on the truth value restric-tion inference technique, POPFNN-TVR (Zhou andQuek, 1996).

An improved MCMAC (MCMAC-ATO) neuralcontrol algorithm with better learning and recall processusing momentum, neighbourhood learning and aver-aged trapezoidal output, has been developed at theIntelligent Systems Laboratory; this addresses theproblem of finite resolution in the MCMAC-memoryand is reported separately (Ang and Quek, 2000). Thechoppy control effort is produced by the low memoryresolution of the MCMAC cells, and can be resolved by

increasing the size of the array cells of the MCMAC.This is done at the expense of hardware RAMrequirement, which currently stands at 16 kilobytes.An improved MCMAC neural algorithm calledMCMAC-ATO has been developed specifically toresolve the deficiency of the low resolution in thememory array during the MCMAC recall process. Thelearning and recall process of the MCMAC-ATO wereinvestigated using the characteristic surface of MCMACand the control action exerted in controlling a con-tinuously variable transmission (CVT) system.

References

Albertos, P., Martinez, M., Navarro, J.L., Morant, F. 1993. Fuzzy

controllers design: a methodology. Proceedings of the Second IEEE

Conference on Control Applications, Vol. 1, pp. 69–75.

Ang, K.K., Quek, C., 2000. An improved MCMAC with momentum,

neighbourhood and averaged trapezoidal output. IEEE Transaction

on Systems, Man and Cybernetic 30 (3), 491–500.

Arzen, K., 1989. An architecture for expert system based feedback

control. Automatica 25 (6), 813–827.

Astrom, K., 1991. Intelligent control. Proceedings of the First

European Control Conference, Grenoble, France, pp. 2328–2339.

Fig. 13. Degradation of the system using MCMAC with process supervision.


Astrom, K., Arzen, K.E., 1992. Expert control. In: Antsakhs, P.J.,

Passino, K.M. Eds. An Introduction to Intelligent and Autonomous

Control. Kluwer Academic Publishers, Dordrecht, pp. 163–190.

Astrom, K., Hugglund, T., 1983. Automatic tuning of simple

regulators for phase and amplitude margins specifications. IFAC

Workshop on Adaptive Systems in Control and Signal Processing,

San Francisco.

Astrom, K.J., Wittenmark, B., 1980. Self-tuning controllers based on

pole-zero placement. Proceedings IEE 127 (3), 120–129.

Brown, M., Harris, C., 1994. Neurofuzzy Adaptive Modelling and

Control. Prentice-Hall, Englewood Cliffs, NJ.

Cohen, G.H., Coon, G.A., 1953. Theoretical consideration of retarded

control. Transactions of ASME.

Commuri, S., Jaganathan, S., Lewis, F.L., 1997. CMAC neural

network control of robot manipulators. Journal of Robotic Systems

14 (6), 465–482.

Jay, F., 1988. IEEE Standard Dictionary of Electrical and Electronics

Terms, 4th edition. IEEE, New York.

Ker, J.S., Kuo, Y.H., Wen, R.C., Liu, B.D., 1997. Hardware

implementation of CMAC neural networks with reduced

storage requirements. IEEE Transactions on Neural Networks 8 (6),

1545–1556.

Kraft, L.G., Campagna, D.S., 1990. A comparison between CMAC

neural network control and 2 traditional adaptive control systems.

IEEE Control Systems Magazine 10 (3), 36–43.

Landau, Y., 1979. Adaptive Control: The Model Reference Approach.

Marcel Dekker Inc, New York.

Lane, S.H., Handelman, D.A., Gelfand, J.J., 1992. Theory and

development of higher order CMAC neural networks. IEEE Control

Systems Magazine 12 (2), 23–30.

Leitch, R., Quek, C., 1991. A behaviour classification for inte-

grated process supervision. IEE Proceedings of the Third

International Conference on Control, Vol. 1, Edinburgh, Scotland,

pp. 127–133.

Leitch, R., Quek, C., 1992. Integrated process supervision architecture.

IEE Proceeding Part D. Control and Application Theory UK. 139

(3), 317–327.

Lin, C.S., Chiang, C.T., 1997. Learning convergence of CMAC

technique. IEEE Transaction on Neural Networks 8 (6), 1281–1292.

Lin, J.-N., Song, S.-M., 1998. Modelling gait transitions of quadruped

based on gait kinematics and CMAC neural networks. Proceedings

of the 1998 IEEE International Joint Conference on Neural

Networks and IEEE World Congress on Computational Intelli-

gence, Vol. 3, pp. 2075–2080.

Mamdani, E.H., 1976. Advances in linguistic synthesis of fuzzy logic

controllers. International Journal of Man–Machine Studies 8 (6),

669–678.

Miller III, W.T., Glanz, F.H., Kraft III, L.G., 1990. CMAS: an

associative neural network alternative to backpropagation. Proceed-

ings of the IEEE 78 (10), 1561–1567.

Quek, C., Leitch, R., 1993. Direct method for model reference adaptive

PI controller using the gradient approach. Proceedings of IEEE

Region 10 International Conference, Tencon 93, Vol. 4, Beijing,

pp. 447–450.

Quek, C., Ng, P.W., 1996a. Realisation of neural network controllers

in integrated process supervision. International Journal of Artificial

Intelligence in Engineering 10 (2), 135–142.

Quek, C., Ng, P.W., 1996b. Fuzzy integrated process supervision.

Proceedings of the IEEE International Symposium on Intelligent

Control, Michigan, USA, September, pp. 319–324.

Quek, C., Ng, P.W., Pasquier, M. 1995. Integrated process super-

vision: a structured approach to expert control. Progress in Artificial

Intelligence, Lecture Notes in Artificial Intelligence, Vol. 990,

Springer, Berlin, pp. 323–335.

Quek, C., Ng, P.W., Pasquier, M., 2000. Application of input

fuzzification in integrated process supervision. Submitted.

Quek, C., Zhou, R.W., 1999. POPFNN-AAR(S): a pseudo outer-

product based fuzzy neural network. IEEE Transactions on

Systems, Man and Cybernetics Part B, 29 (6), 859–870.

Rosenblatt, F., 1961. Principles of Neurodynamics: Perceptrons and

the Theory of Brain mechanisms. Spartan Books, Washington DC.

Wong, Y., Sideris, A., 1992. Learning convergence in the cerebellar

model articulation controller. IEEE Transaction on Neural Net-

works 3, 115–121.

Zhou, R.W., Quek, C., 1996. POPFNN: a pseudo outer-product based

fuzzy neural network. Neural Network 9 (9), 1569–1581.

Ziegler, J.G., Nichols, N.B., 1942. Optimum Settings for Automatic

Controllers. ASME, New York.


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