fuzzycontrolofthecompressorspeedinarefrigerationplant-外文文献(编辑修改稿)

fuzzycontrolofthecompressorspeedinarefrigerationplant-外文文献(编辑修改稿)内容摘要：

y saving obtained when a pressor speed control isused. The tests, which lasted 2 days, have been realized forthe R407C and the R507.4. Fuzzy logic in the pressor speed controlThe fuzzy logic represents a methodology that allows usto obtain defined solutions from vague, ambiguous oruncertain information. For this the fuzzy process is verysimilar to that of the human mind capable of finding definedconclusions starting from approximated information anddata. In contrast to the classic logic approach, that requiresan exact definition of the mathematical model equationscharacterizing the phenomenon, the fuzzy logic allows us tosolve problems not well defined and for which it is difficult,or even impossible, to determine an exact mathematicalmodel. Therefore, the human experience and knowledge isnecessary for this type of modelling. In particular, the fuzzylogic is a valid alternative for the solution of nonlinearcontrol problems. In fact the nonlinearity is treated bymeans of rules, membership functions and inferentialprocess, that ensure simpler implementations and minordesign costs. On the other side the linear approximation of anonlinear model is simple enough, but it has thedisadvantage to limit the control performances and canresult, in some situations, expensive. Moreover, the fuzzycontrollers are robust and allow us to realize improvementsor changes in a very simple way by means of the use of theother rules or the membership functions. Many examples offuzzy control can be found in some recent applications. Inparticular, in the heating ventilation and airconditioningindustry there are various fuzzy control applications of theair temperature and humidity [25–28]. The design of afuzzy controller requires three essential phases. The first isto establish the input and output variables. The second is todefine the membership functions for the input and outputvariables. The last is to select or formulate the control rules.The main goal of this paper is to determine a fuzzycontroller capable of regulating the pressor electricmotor supply current frequency. In Fig. 2 a block diagram ofthe fuzzy control process of the mercially avalaible coldstore air temperature is reported. In particular, the figureshows a twoinput oneoutput fuzzy controller. The inputvariables are the temperature difference between the setpoint temperature and the real temperature of the air in thecold store 240。 DT222。 and the derivative of this temperaturedifference with time 240。 d240。 DT222。 =dt222。 the fuzzy output variable isthe frequency of the supply current of the pressorelectric motor 240。 f222。 : The fuzzy logic is based on thedetermination of the fuzzyset that represents the possiblevalues of the variables. The fuzzy theory with respect to thetraditional logic theory, according to which an element canbelong or not to a particular set, allows the partialmembership of an element to a set. Each value of thevariables is characterized by a membership value whichchanges with continuity from zero to one. Thus, it is possibleto define a membership function for each variable thatestablishes the membership rate of a variable at a certain set.From an operative point of view, a controller fuzzy receivesthe values of the input variables, performs some operationsand determines an output value. This process is characterized by three principal phases: fuzzification, inferencemechanism and defuzzification. The fuzzification processallows to transform a value defined into a fuzzy value。 theinference process determines the output fuzzy by means ofthe rules fixed according to the experimental reality。 thedefuzzification process permits to transform the fuzzyoutput into a defined value. The main difficulty of thefuzzy logic is connected with the necessity of a goodspecific experience in the design and the building of a fuzzycontroller. So, as for the regulating parameters someexperimental considerations have allowed us to set thecontrol variables of the reciprocating pressor speed. It isclear that the choices of the rules and membership functionsof the controller can be properly changed. However, it is tobe considered that it is certainly convenient to control fromthe energy saving point of view the pressor speedbecause it works at lower frequencies, but in this situationthe time required to obtain the setpoint temperature will beTable 1Transducers specificationsTransducer Range AccuracyCoriolis effect mass flow rate meter 0–2 kg/min ^%RTD 100 4 wires 2100 to 500 8C ^ 8CPiezoelectric absolute pressure gauge 1–10 bar。 1–30 bar ^。 ^ Wattmeter 0–3 kW ^%Electric energy meter 360–420 V。 0–16 A ^% .。 ^% .C. Aprea et al. / International Journal of Refrigeration 27 (2020) 639–648642major in parison with the time necessary when thepressor works at a 50 Hz nominal frequency. So, it mayhappen sometimes that even when the pressor works forfrequencies lower than that nominal ones the energy savingexepected may be partially obtained because the pressorhas worked at lower frequencies indeed but for too muchtime. So, in order to regulate the working time of thepressor at lower frequencies it is important that, whenthe fuzzy algorithm input and output variables membershipfunctions are to be defined, the choice of the subset numberand of its wideness has to be proper and guided by theexperimental knowledge. As for the the choice of the rules itis necessary to do similar consideration. For this reason thealgorithm membership functions and rules suggested fromthe authors have been experimentally verified. Table 2shows the rules fixed to set the algorithm and the five fuzzysubsets used to characterize the input and output linguisticvariables marked with the following labels: very low (VL),low (L), medium size (MS), high (H) and very high (VH).As to tune the membership functi。