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Control metliocSs series Part 5

Advanced Process Control:

Fuzzy Logic andExpert Systems

Examining the results of applying fuzzy logic and expertsystems for reactor control.

Applying fuzzy logic to control thereactor using only the three exist-ing process measurements—outputflow, composition, and tempera-ture—imposes a severe perfor-

mance limit on the system. Without a mathe-matical derivative capability in the rule syntaxthe system can react to the current values of themeasurements, but not to how fast they arechanging. However, derivative action is veryhelpful in controlling variables that respond witha dominant capacity lag. For this application,product temperature is a lag dominant variable.

For this reason, the design needs a fourth con-trolled variable —incremental temperaturechange. This allows the design to include logicthat reacts more strongly when the temperatureis changing than when it is steady. The setpointfor this variable is 0, meaning that the systemshould seek to keep temperature constant at itsabsolute setpoint.

Reactor controls use three subsets for the con-trolled variables, which are applied to the mea-surement error to accommodate a variable set -point. The controlled variable error subsets are:small negative (NS), zero [ZE), and small positive(PS|.

The design uses five subsets for the outputvariables, to accommodate the number of com-binations of the 4 controlled variables. The sub-sets that describe the manipulated variablechanges are; medium negative (NM), small neg-ative|NS), zero (ZE), small positive (PS), andmedium positive |PM).

The tiesign also includes logic to decoupleproduct flow and composition. For example, ifboth product composition and product flow arehigh, then the logic should force a decrease inboth ingredient flows. But if the % A is high but

product flow is on target, then the logic shouldforce a reduction in ingredient A and an increasein ingredient B to change composition withoutchanging total flow rate,

Table 1 shows the rules for changes in the flowsof ingredients A and B to control product flow andcomposition. (In this context, positive errormeans measurement is higher than setpoint.)

Matrix intersections define the logic of therules. For example, the combination of a positiveerror in total flow (PS) and negative error inproduct %A (NS( requires a small decrease in Fa(NS) and a medium decrease in Fb (NM), Thereare a total of nine combinations. The rule foreach combination forces two control actions.

Table 2 shows a similar rule set for controllingproduct temperature and its incrementalchange.

For example, when temperature error is posi-tive (PS), but temperature change is negative(NS), then steam flow should not change (ZE),Again, there are nine combinations, but each hasonly one control action. Since steam flow doesnot affect either product flow or composition,this part of the logic does not require any decou-pling responses lo change the ingredient flows.

At four-second intervals, this fuzzy logic con-troller "fuzzifies" control inputs, evaluates bothrule sets using the fuzzy inputs to generate fuzzyoutput variables, and de-fuzzifies these values toobtain values for incremental changes in allthree manipulated flows.

Fuzzy logic control perfformanceThe trend graphic shows the response of thefuzzy controls to the same changes in produc-tion rate and product composition as were usedfor the previous control applications.

Certainly, this fuzzy logic application provides

AT A GLANCE

• Product flow andcomposition

• Rule set forchanges

• Operator impact

• Rule-basedcomparisons

ONLINERead this atwww.controleng.com/archive,Sept. 2005, to link to priorarticles in this series andmore about fuzzy logic.

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adequate control. The question is; how does itcompare to basic regulatory control and advancedregulatory control of the same process?

Table 3 shows that the performance of thisfuzzy logic controller is worse than either formof regulatory control, with one exception. Thefuzzy logic provided superior control of productcomposition during a production rate change,showing an index of 0.003. This is 5 times bet-ter than advanced regulatory control. This isbecause the requirement for maintaining com-position is so straightforward. The two ingredi-ent flow loops have identical dynamics, and thereactor is a pure delay. As long as the logicchanges the ingredient flows simultaneouslyand in the proper proportion, product composi-tion will remain constant.

By every other measure, performance of thisfuzzy control system is relatively poor. Since thelogical design lacks any counterpart to feedfor-ward for temperature control, the temperatureperformance index for production rate changes is1.35, 48 times worse than advanced regulatorycontrol. On a production rate change, the tem-perature drops almost to the specification limit of120 °F, with potential to make off-spec product.

Table 1. Rule set for changes in Fa and Fb

Total Flow error PS

Total Flow error ZE

Total Flow error NS

%A error PS

Fa = NM

Fb = NS

Fa = NS

Fb = PS

Fa=PS

Fb=PM

%A error ZE

Fa=NS

Fb = NS

Fa = ZE

%A error NS

Fa = NS

Fb=NM

Fa=PS

Fb = NS

Fb = PS

Intersections in this matrix define the logic of the rules for changes in theflows of ingredients A and B to control product flow and composition.

Table 2. Rule set for changes in steam flow

Temp Change PS

Temp Change ZE

Temp Change NS

Temp error PS

Fsteam= NM

Fsteam = NS

Fsteam = ZE

Temp error ZE

Fsteam = NS

Fsteam = ZE

Fsteam = PS

Temp error NS

Fsteam = ZE

Fsteam= PS

Fsteam = PM

Similar to the rule set in Table 1, this rule set is for controlling product temper-ature and its incremental change.

Because the design does not include a compo-sition change variable, the logic could not provideany equivalent to a derivative function for com-position control. Consequently, the index for com-position setpoint changes was worse than for basicregulatory control, 2.7 vs. 1.79. Since the temper-ature control logic is essentially the same as sim-ple feedback control and the temperature changevariable allows a derivative-like response, the tem-perature index for composition setpoint changes isclose to that for basic regulatory control, andmuch worse than advanced regulatory control,

A much simpler fuzzy logic controller couldhave been applied. If the product flow and com-position control logic had not included decou-pling actions, the overall solution would havebeen functionally equivalent to single PID loopswithout derivative function, and the perfor-mance would have been worse in all aspects.

Likewise, a more complicated fuzzy logic con-troller could have been applied. Feedforward logiccould have been included in the temperature con-trol rule set by adding variables for changes in theingredient flows. Similarly, the logic could havebeen expanded to include a composition and/orflow change variable, or variables related to ingre-dient temperatures. But with the addition of everynew variable into the design, the number of com-binations and rules increases exponentially.

Further, there is no way in fuzzy logic to pro-vide the equivalent of dynamic compensation.Changing the ingredient flows does not imme-diately affect product temperature. For propercompensation, the logic would have to be capa-ble of delayed actions, which would require cre-ating timers and signal queues. The solutionwould have to be much more complex.

Expert systemsFuzzy logic is a well-defined and mature tech-nology. Its success depends on the quality ofthe logic implemented in the rule set(s). In con-trast to fuzzy logic, there is no precise defini-tion for expert system technology. The onlyaccepted definition is that an expert system isone whose performance can't be distinguishedfrom that of a human expert.

It is an axiom of process control that no auto-matic control system can perform as well as anexpert human operator who is 100% focused oncontrolling a specific variable with manual con-trol. Human beings are smarter than any com-puter system. They can integrate a wide rangeof dynamic and steady-state information thatmay not be available to a control system intocontrol action decisions.

But this level of intelligence can't be designedinto a control system until it is obtained from the

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Fuzzy logic reactor control trends

Prod CompS29Q0. Comp SP77.88 %A

JB3QQ2 ProdTempS»G2 Temp SP121.3 degF

200 ARC FLC Reactor'n«nProd Flow 100

. Flow SP 10097.64 GPM 115.8

8aoo8087.06

124.9125128.9

Calc Delta T 0.005Temp Chg SP 0

-2.761 degF Z770

19.9923.20

80.0092.80

2000323544

Zoom

fngrB12.94 GPM

A1000 IngrA77.35 GPM

A.1002 Steam18921 #/hr

The fuzzy logic controllerprovided superior con-trol of product composi-tion during a productionrate change.

expert who has it. The design and success of anexpert system relies entirely on the skills of the"knovv'ledge engineer" v̂ rho extracts process con-trol information from those who are identifiedas experts.

Furthermore, not all experts are createdequal. Not all experts can clearly explain whatthey know. They will often disagree, or havevarying degrees of correct understanding.

Because there is no precise definition of thetechnology of an expert system, it is impossibleto quantify what its performance can or will be.Simply put, its relative performance will only beas good as the engineer's skills, process under-standing, dedication, and budget can make it.

Operator impactA rule-based system uses a set of concepts andtools that will probably be unfamiliar to

process operators.Depending on theeffort put into itshuman interface,such a system willappear more or less asa black box. Muchdepends on whetherthe rule-based systemis used in closed-loopcontrol, or functionsin an off-line advisorymode. They will havelittle or no under-standing of how itworks, how to adjustits behavior, or whatto do if they disagreewith its decisions,other than simplyignoring or disablingit. This makes it even

more important to involve the operators in thedevelopment of the system.

Final assessmentThe objective of a rule-based system is to achievecontrol through a set of rules that imitates theanalysis and decision-making process of an expe-rienced human operator. But there is very littlethat is standard about how humans think andmake decisions. For this reason, rule-based sys-tems are very individual and unique solutionswith a variety of complexity and scope.

This is an advantage and a disadvantage. Rule-based systems can be more flexible and creativethan other control technologies. They provide aconvenient way to introduce non-mathematicalconsiderations into a control solution, and caninclude inputs that are difficult or impossible forother technologies to consider. Rule-based con-

Table 3. Fuzzy logic reactor control performanceChange production rate

Control technology

3asic regulatorycontrol

Advanced regulatorycontrol

Advanced fuzzy logiccontrol

CompositionISE

.53

0.015

0.003

TemperatureISE

.28

0.028

1.35

TotalISE

.81

0.04

1.35

CompositionISE

1.79

1.83

2.70

TemperatureISE

0.55

0.08

0.56

TotaISE

2.34

1.89

3.26

Change product composition

Fuzzy logic controller system performance compared to basic and advanced regulatory control of the same process.

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trol systems can be the best solution formixed systems where control logic musttake several kinds of conditions intoaccount, and any system is better thanfully manual control.

But this flexibility becomes a liabilitywhen it is not needed. Rule-based sys-tems are a poor replacement for evensimple PID loops, and they can quicklybecome too complicated when morecomplex control structures are needed.There is no simple way to deal eitherwith interactions among process vari-ables or process dynamics, because thelogic of the rules generally evolves fromsteady state responses. As the number ofrules increase, either in the originaldesign or through modification overtime, there is a strong potential for intro-ducing unrecognized conflicts into theoverall logic. Until they are debugged,this can easily degrade production oper-ations. Such a conflict may not be imme-diately evident and its consequence canappear unexpectedly,

Further, a fuzzy logic or expert sys-

tem applied for closed-loop control stillhas to be tuned, and it is just as vulner-able to the problems created by varyingprocess gains as any other system. A sys-tem under rule-based control can stilloscillate, and there are no widelyaccepted procedures for tuning thesesystems. Often, the designer is the onlyone who understands the rule parame-ters well enough to tune them. Unlessthe rule structure is fairly simple, theirperformance is likely to degrade whenthe designer is no longer available tomaintain the system.

Generally speaking, the performanceof rule-based systems for typical processcontrol problems is not as good as math-ematical algorithms, which are morestandard, efficient, and powerful. c«

77ie next installment in this series willintroduce the concepts of model predictivecontrol.

Lew Gordon is a principal applicationengineer at Invensys; wv^w.invensys.com

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