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A New Class of Adaptive Fuzzy Control Systemsapplied in an Industrial Thermal Vacuum ProcessJ. E. Araujo Filho l9

ernestoblit.inDe. brSandra A. Sandri

sandri@lac. npe.brElbertE.N.Macau

elbertblithpe.br

Integrating an d Testing Laboratory.-LIT*Cornputer Sci. and Applied Math. Associated Lab - LACBrazilian National Space Research Institute - NPE12.227-010-Si0 O S ~ ampos- SPBRAZIL

Department of Computer ScienceUniversidade Cruzeiro do SUI- UnicsulSi0 Paulo - SPBRAZIL

Abstmct - A feasible solution to the problem of controllingthermal vacuum chambers automatically to satisfy testingrequirements in the space sector is considered in this paper.The design of appropriate controllers is not a trivial task dueto intrinsic time delay and changing dynamics related tovariable operating conditions in thermal vacuum tests. Thefuzzy reference gain-schedulingcontrol approach (FRGS) is aconcept that has been under development to substitutespecialists in controlling thermal vacuum systems. Inthis newclass of adaptive fuzzy controller the parameters are adjustedon-line by modifying the shapes of the membership functionsaccording to different operational conditions. Its application isnot limited to controlling thermal vacuum processes. It can beapplied to control any industrial problems or to modeldynamics of systems, and it can be used in decision-makingtasks.

211. The new sort of controller presented here combinesfuzzy control and gain-scheduling control approaches bysetting up a new kind of adaptive control in which theshapes of the membership functions change according todifferent operational conditions. This kind of controllerincorporates the expertise of the human expert acquired inpast experiences to figure out an approach tocontrolhpervise thermal-vacuum chambers automaticallyin accordance with the requirements established bystandards for the space se ctor [22][23]. Th e fuzzy referenc egain-scheduling control approach is not applicable only tothermal vacuum process. There is potential application tofactory automation for those processes that, for instance,are not linear and whose dynamics change with timeaccording to operational conditions, and/or present time-delay.I. INTRODUCTION

11. BACK GROUN D AND PROB LEM FORM ULATIONThermal vacuum chambers are used during thequalification process of space product development. Theysimulate environmental conditions in space of vacuum andthermal load to guarantee that a given satellite will operateefficiently when subjected to real environments differentfrom those on earth [l].The main problem is when it isnecessary to decide which is the best control approach toregulate the thermal vacuum testing process.The thermal vacuum process presents different heatingand cooling rates for distinct payloads [2]. Moreover, theprocess develops several different operational thermalconditions when following different reference temperaturevalues at distinct moments of time. Since each payloadpresents various thermal behaviors, the task of modelingthe chamber and space devices is complicated. Thechambers nonlinear dynamics and the payload runningtogether suggest that the use of conventional controllers isnot appropriate. Nowadays the control of thermal vacuumchambers is conducted by experienced operators. In orderto carry out safe, efficient, high quality and low costtesting, it is of fundamental importance to find suitablesolutions that support test operators [3].

An emerging fuzzy reference gain-scheduling control(FRGS) approach [4] is presented in this paper as anefficient set of autonomous actions to supervise andmaintain safe hot or cold operating temperatures. Fuzzygain scheduling control (FGS) systems have been studiedand their application demonstrated in numerous papers [5 -

Simulated orbital life and space environments of spacesystems include thermal vacuum tests, which reproduce theconditions of expected post-launch environments. Once inspace, satellites are exposed, but not limited, to sunshine,Albedo radiation, earth radiation, shadow/eclipseconditions, and earthshine infrared.A thermal vacuum system consists of a chamber, ashroud (set of pipes) which transmits heat or cold byradiation, and some devices and auxiliary equipment ableto determine specific conditions for the test to be performed(Fig. 1).

0-7803-7241 7/0 1/$10.00 (~ )2 0 0 EEE 425

t SV2w=wsv1Fig. 1-Thermal Vacuum Chamber Diagram

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The operation of the thermal vacuum chamber [24] isdescribed next. A vacuum environment is accomplished bythe use of two separate pumping systems, after what thetemperature is modified. The first pumping system is asingle, dual stage, rotary vane, mechanical pump thatproduces low pressure inside the cham ber. Once the desiredpressure is reached, a high vacuum is obtained by using acryogenic vacuum pump with closed cycle heliumcompressor. The global system produces pressures aroundl ~ l O - ~orr to simulate the vacuum present in space. Whena satellite is in a high vacuum environment, the thermalcycle starts. Modifying the temperature inside the shroudsimulates that situation. The operation of the thermalshroud is achieved by means of a re-circulating, d ense, andgaseous nitrogen (GN2) system. To maintain nearlyconstant heat transfer properties throughout the wide rangeof system operation, a constant density system is utilized.Cooling the circulating gas stream is accomplished byspraying liquid nitrogen (LN2) into the circuit whileresistance type heaters mounted inside the piping networkprovide heat as required.In the thermal vacuum system used at the BrazilianNational Space Research Institute (INPE), the originalcontroller was designed to control the temperature on theshroud (Fig. 2). The GN2 thermal system is accomplishedby using a dual output, time proportioning, heat-cool, andPID controller. That temperature controller sends outcontrol set points to the GN2 pressure PID controller tokeep constant heat transfer characteristics. The systempressure is adjusted to the required level by modifyingventing nitrogen gas through the venting control valveor by switching the LN2 upply valve (SV1) as canbe seen in Figures 1and 2. Nevertheless, requirements forthe space sector establish that the controlled variable is notthe temperature on the shroud, but the temperature at thespecimen surface.Conventional control systems are not appropria te for thistask since the system to be controlled is highly nonlinear,presents time-delay, and changes its dynamic behavior inmany different situations. Thermal vacuum chambers areinherently nonlinear because radiation is basically thesource of heat transfer between the payload and shroud,and depend on temperature (T 4 [3] as the equation (1)shows:

where: dTpl/dt is the payload transition rate,Tpl is the payload average temperature (absolute),Tsh is the shroud average temperature (absolute),Mpl is the payload mass,Cpl is the payload heat capacity,CT is the Stefan-Boltzmann's natural constant,E is the emissivity 1absorptivity of a gray body,A is the radiating area.

When the equation (1) s linearized, it is possible to notethat various thermal operational conditions correspond tothe reference levels (set points) used during the spaceproduct qualification.

Fig. 2-Original Thermal Vacuum Control SystemThe modification in dynamics occurs independently oflinearizations considering stationary behavior or off-equilibrium nominal trajectory. The time-delay isconcerned with the thermal optical characteristics of thespecimen undergoing the test as well as its physicalcharacteristics, best described by specific mass, specificheat, and thermal conductivity.Those features hinder the ma thematical task of modelinga chambe r and a payload tog,ether. Thus, the design of

controllers to automatically command the thermal vacuumchamber is not a trivial task. To satisfy the testrequirements and the necessities of space standards,nowadays operators control thermal vacuum systems in anon-automatic way (Fig. 3). A question that arises iswhether it is possible to design ,an automatic control for thisprocess; and if the answer is positive, the next question iswhich control approach would he more suitable.

111. FEA SIBL E SOL UT IONA feasible solution to the problem of automaticallycontrolling the thermal vacuum chamber satisfyingrequirements of space sector is to use the expertise of test

operators. An approach able ito imitate human commonsense reasoning to define set points in order to controlsystems is f u z z y controllers [25]. Fuzzy control systems arenonlinear and suitable for dealing with certain amount ofnonlinear processes as occurs in thermal vacuum chambers.However, such a design also needs to cope withnonlinearities over a variety of operating points determinedby the reference values of tempe rature during the test.

I I

I / IFig 3-Current Thermal Vacuum Control Diagram

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The use of adaptive controller is an alternative to dealwith the problem of non-linear, time-varying, time-delaysystems working on several operational conditions. A newclass of nonlinear adaptive fuzzy controllers that considersthose conditions named fuzzy reference gain-schedulingcontrol (FRGS) system was proposed in a previous paper[4]. Its basic idea is to merge the nonlinear characteristicsof the fuzzy control method and the adaptive featuresconcerned with the gain-scheduling concept, by changingthe shapes of the membership functions, according todifferent operational conditions in real time (Fig. 4). Intruth, the fuzzy gain-scheduling control (FGS) system hasbeen described by several manners in the literature [5-211.However, most of them work directly or indirectly byselecting local parameters of control in a gain pollcomputed earlier, while FRGS controllers compute theparameters on-line to generate control surfaces, dependingon the reference.The FRG S technique is based on four d ifferent concepts:(1) Adaptive fuzzy control systems [25]; (2) Gain-scheduling control systems [26]; (3) The general class ofsystem equation which presents s tep behavior; and (4) Th eselection of several operating points indexed by somecombination of reference state trajectories [27]. FRGSprofit from traditional adaptive controllers, the idea ofaltering scaling factors, and the ability of modifying fuzzysets. The notion of adjusting the parameters of thecontroller (gains) directly as a function of the operatingconditions (scheduling) comes from the gain schedulingcontrol system. The main characteristics of fuzzy referencegain-scheduling control systems are their ability to:Incorporate the expertise of human operators;Include knowledge about variation in the conceptsunderlying the membership functions;Adapt control surfaces as required by operationalconditions, mainly determined by the reference;Permit parameters to change homog eneously, as a scalingfactor;Allow parameters to modify independently or even stayconstant;

A

- Be used both in the coding (fuzzification) and decoding- Be employed with any of the rule based models existing- Adjust the control parameters on-line.

(defuzzification) activity;in the literature; and

N. UZZYREFERENCE GAIN-SCHEDULING (FRGS)CONTRO L IN A THERMAL-VACUUM SYSTEMThe basic structure of fuzzy controllers consistsbasically of a fuzzification interface, a know ledge base, andan inference mechanism comprised of fuzzy implicationfunctions and a defuzzification interface. Fig. 5represents ageneral fuzzy control diagram that has been combined withthe FRGS concept by using the idea of information flow a sit is employed at LIT/INPE (www.inpe.br) in order to assisttest operators in the control of a thermal-vacuum cham ber.This control approach is currently under developmentand the diagram incorporates changes that have occurred sofar in the search to find a suitable controller to regulate thewhole process adequately. Experiments using an earlier

knowledge base to form a fuzzy reference gain schedulingcontrol system were presented in [4]. This paper showsrecent results concerned with a new set of rules andmembership functions described in T able I and 11. This u p-to-date knowledge base represents a refinement of thefuzzy control used to supervise or control the thermal-vacuum system.Although the FRGS concept may be used in both thecoding (fuzzification) and decoding (defuzzification)modules, the proposed idea was employed here only withthe input m embership functions at the fizzificutzon (coding)interface. In this paper the fuzzification interface uses theerror, e, and the temperature change on payload, ATpl, asinput variables. The error, which is the difference betweenthe desired reference and the real temperature value on thepayload, is associated with both constant linguistic termsand adaptive linguistic terms.

-Q(l)-fl(1) - k 10 k fl(l) Q(1) iA !

-a(loo) -fl(loo)Fig. 4 -Adaptive Membership Function

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Fig. 5 -Fuzzy Reference Gain-Scheduling Control Diagram used withThermal Vacuum Chamber

INPUTVARIABLE

error*

Adaptive sets change their support and core dependingon the desired temperature, without interfering at fixedfuzzy sets. This adaptation works on transient periods intwo categories: (1) When error is far from the reference(100% - 0%), and (2) When the error presents anintermediary value (50%* 0%). In turn,when the erroris close to the reference (20% - C), the control law ismore restrictive to guarantee the temperature on thepayload will not overshoot or damage the payload. Theother input variable is associated only with the traditionalclass of fixed m embership functions (Table I).Th e inference (reasoning) mechanism is the process offinding the value of the output variable (conclusion). Thatprocess deals with the set of rules and the inputmembership functions, along with f u z z y implicationfunctions (also known as f u z z r reasoning or approximatereasoning) and a defuzification (decoding). Since theemergence of fuzzy controllers in 1974, the inferencemechanism has been sorted into classical and interpolationtechniques, representing two different kinds of fuzzyreasoning. The major difference between them is that theclassical approach uses fuzzy sets, whereas theinterpolation approach employs (linear) functions in theconsequent of fuzzy control rules. Classical fuzzycontrollers include Mamdani and Larsen models whileinterpolation techniques comprise Takagi-Sugeno andTsukamoto models interface [29-311. The controller in thispaper employs a classical Mamdani approach in the fuzzyimplication functions while the Tsukam oto model is used inthe defuzzification (decoding) interface to compute theincremental value of the control action (Au) and thus, thecrisp output (U). Fig. 6depicts the interpolation Tsukamotoapproach employed in this FRGS control system approach.

RANGEEMBERSHE'FUNCTIONA h p t i v e (100% - 0%)*(fRef)Adaptive (50%- 0%)*(?Ref)Adaptive-Cons,tnnt (20%)*(+Ref)++ (AS C )ConstantConstant

(5C )++ (*2 C )Constant (22 C ) (*l C )(+1 C )- 0 C)

4-AU =wl*AUl+~2Aulwl+w

Fig. 6-Tsnkamoto's inference mechanism

A Tpl* error membership functionsare positive or negativeThe basic elements of the output variable are shown inequation (2):. .

U = U d d + AUwhere:- Uold is the previous set point adjusted at the con troller;- Au = @(A* ) is the incremental value of output and is

represented in Table I1by the set of eq. Up and Un.- hAU)s the membership function related to thedefuzzification interface- K is a constant value related to the necessary energy toreach the desired transient.

Th e database, which determines the membershipfunctions used in the fuzzy miles, and the linguistic rulebase, which contains a selection of if-then fuzzy rules,composes the knowledge base 1:28-30]. The components ofthe knowledge base are defined by the experience ofthermal-vacuum operators ancl they are summarized inTables I and 11.The control actions related to positive values ofreference levels use a set of elquations determined by therequired electrical power to maintain the desired transienton payload. Negative values of reference levels use asimilar set of equations, but different from the positive one,they are settled by the amount of necessary gaseousnitrogen to keep the desired transient on payload. Bothpositive and negative set of equations are presented inTable 11.

Transientet point

(U))))== p(u)+UoldO+Uold 1,5"/m"ImHeating 9)))= 4p(u)+Uoldp(u)+Uold 0,5"/mImControl I)) =2p(u)+Uold 0,2"/m(4 U)) = p(u)+Uold 0,08"/m

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A dynamic response related to a step input signalbehavior can be seen in Fig. 7 hereafter. The dynamicbehavior describes a smooth temperature change onpayload. There is no overshoot as required to avoiddamaging any specimen under test. Moreover, there is nostrong oscillation around the temperature reference eventhough an approxim ate oscillation of 3 degrees is allowed.Nevertheless, these new database and linguist rule base donot solve all of the requirements for a system to becontrolled. There is still time delay from the input signal inthe transient response and the rise time is a little bit slowerthan desired. Despite the existence of these smalldrawbacks, the positive results of using the fuzzy referencegain scheduling control system outnumber thedisadvantages.

1 PayloadTempeature -Reference IFig. 7 -Transient response fora step signal

The designed controller conducted the thermal-vacuumchamber test to reach the reference without using amathematical model that is hard to be found since thedynamic behavior is determined by each payload under test.

V. CONCLUSIONThis paper presents a methodology based on a gain-scheduling approach and adaptive fuzzy controller tocommand an industrial thermal-vacuum process. It was alsopointed out that the design of the controller is underdevelopment, but the control system has already beenachieving positive results.This sort of adaptive fuzzy control permits theapproximation of human reasoning by using fuzzy theory tocontrol nonlinear and complex systems. The idea of usingthis controller is to substitute specialists in the assignmentof controlling systems in order to carry out safe, efficient,high quality, and low cost testing. Such a controller needsalso to deal with nonlinear characteristics over a variety of

operating points determined by the reference temperaturevalues of the test.This concept has been named fuzzy reference gainscheduling control (FRGS) systems. This adaptivecontroller has been employed with input membershipfunctions that adapt their supp ort and co re as the referencechanges in step levels, along w ith the inference mechanismcomposed by Mamdani implication functions andTsukamoto defuzzification interface.

Th e benefits of applying such a controller are describedby its ability to control nonlinear systems without anymathematical model, incorporating the intrinsic dynamicbehavior features of thermal processes especially whenvacuum is present.Beyond those q ualities, this class of control systems canbe applied, but is not limited to, other industrial processes,can be employed to model dynamics of systems, or evencan be used in decision-making tasks.

VI. ACKNOWLEDGMENTSJ.E.A.F., and S.A.S. ackno wled ge supp ort from Brazilianresearch funding agency CN Pq w ith grants 381.212197 and520.176196-0, respectively, and E.E.N.M. with grants4.647.335100-9 and 300.600100-3.

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