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A kind of genetic fuzzy control algorithm and its application in distilling tower pressure controller

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Pressure controlled object of distilling tower is complicated, has more effective factors and strong interference, its modeling is difficult. so normal control algorithm can't control it with satisfactory quality. To overcome this difficult, a
  Pmceedngs zyxwvu f /E€€ zyxwvutsr NCON02 zyxwvut A KIND OF GENETIC FUZZY CONTROL ALGORITHM AND ITS APPLICAION IN DISTILLING TOWER PRESSURE CONTROLLER zy ang XIONG Dr.’ cniarm~mber1EEE;TIMO R. NYBERGDR.’, Zhi-yu ZHANG3, and Guang-yu X10NG3 ’Nokia Business Infmtlucture, M%M team, FIN-24101, Salo, FINLAND Email: xiong.gang@, ’.3.‘Automation and Control Institute, Tampere University of Technology, FIN-33101,Tampere, FINLAND Abstract: Pressure controlled object of distilling tower is complicated, has more effective factors and strong interference, its modeling is diffbxlt, zyxwvutsrq o normal control algorithm can’t control it with satisfactory quality. To overcome this difficult, a kind of genetic fuzzy algorithm is put forward. While fuzzy logic mimics the human’s imprecise reasoning, the genetic algorithm mimics the evolution of the nature. an optimum point always can zyxwvuts e found by the genetic fuzzy algorithm. Experimental result proves that, even through there is strong interference, the algorithm still can ensure actual pressure value to fellow the given pressure value quickly and steadily. i Key zyxwvutsrqpo ords: distilling towcr, pressurc control, genctic algorithm, fuzzy logic, gcnctic fuzzy algorithm 2 1 Fuzzy Logic System The basic principle of fuzzy logic system in the form of hybrid genetic fuzzy logic (HGFL) is shown as Fig.2. The fuzzy logic system execute the mapping from x=(e,i)EXc ’ to AUEUCX wher(e,e) z re accurate measurement values, which re fuzzied by the fuzzy module, re dc-fuzzicd by de-fuuy module. Every input is grouped into one of seven fuzzy sets, i.e. NL (Negative Large), NMWegative Medium), NS(Negative Small), ZE(Zero), PS(Positive Small zyx   , PMfPositive Medium), PL(Positive Large). Actual value can be gotten by input subordination function. For simple optimization process, every subordination function can be defined by a, and zyxwv i : 1 xlqand subset=NL 0 xlqand subsetf NL 1.INTRODUCTION The oressure (PC302 in tower 2) control svstem of 2 (x-q.)yq <xl- 4 a) olvent recovering device [I1 is the object mainly discussed in this paper. PC302 is set by energy optimization system of upper controller, the pressure control system mainly keeps &a,,/ ) =-  8. a.) 2 the actual value of PC302 follow uu &e set value buicklv ” , r ,., ll\ 2 . and accurately. The control valve bf PC302 is the output valve on the top of tower 2 (beside Al). Because.input 4x’q’p,)’~(p, -x gy V YI material fluctuates greatly, it is difficult to keep PCi02 stable. Pressure controlled object of distilling tower is complicated, has more effective factors and stron q.,h.)={ interference, its modeling is difficult, so normal control algorithm can’t control it with satisfactoly quality I] TO Overcome this dimcult, a kind of genetic ’. 1 x>A.and subseePL 0 x+fi.and subsei PL Where xis eor e. Fig3 and Fig.4 separately are the fuzq subset where the subordination hnction c and e arc 21, and ug separately. lgorithm is put fowd where genetic algorithm is used to change the. subordination function. While fuzzy logic mimics the human’s imprecise reasoning, the genetic algorithm mimics the nature evolution: Random search 1 ur NM NS ZE ps PM p~ method is used by GA an optimum point always can be found by the genetic fuzzy algorithm. 2 THE DESIGN RINCIPLE OF GFLC Recently, more and more people use CA to optimize fuzzy . logic set ‘?I its basic princi le is shown in Fig. 1. Hybrid 0 0 Genetic Algorithm (HGA) R is an optimization tool, its emin e mar operation s based on real nninber, or float number, and some special knowledge. So, fuzzy logic set will change with the change of GA operation. Fig.3 Input subordination function of e 0-7803-7490-8/02/$17.00O2002 IEEE. 1455  To get fuzzy output from the subordination functions Of input e and zyxwvutsrqp   a set of fuzzy control rule related to fuzzy execution and combination rule inference. Normal fuzzy rulcs zyxwvutsr se IF.. .THEN.. structnrc For cxamplc: IF e is NS and zyxwvutsrq   is PS: then AU is ZE. Where. NS, PS are the fuzzy subset of e and The actual output value of zyx uzzy controller is computed from special input signal by specified fuzzy rule. To make System zyxwv e fuzzy output set de-fuq process is necessary, correspond output subordination function (Fig.5) is defined inequation 1. separately; zyxwv   correspond seven fuzzy subsets, i.e. NL, NM, NS, zyxwvu   S, PM, PL. A fuzg model based on fuzzy rule is described in Table. 1. zyxwvutsrqpon R N m NS ZE PS PM PL Table.1 IF-THEN rule 1 The rules in Table. lcan be expressed as: RI :If e is NL and e is NL then Au is NL R, :Ue is NL and e is NM then Au is NL R,:UeisNLand e isNLthen,Atr isNL R,, :U e PL and e is PL then Au is PL ... ... or, R =RI UR2 U...UR4, (2) zyxwvu t/, NLNM NS ZE PS PM PL , A% Aur 0 Fig.) Subordination function of output So; the precondition value of every rule is computed out, ind applied to itsxonclusion parl. The result of every f.uzy subset is assigned to the output of every rule. Triangle subordination function of output is gotten from: uiA,,,(x) = min{z{,(x),u,(x)} (3) Where: A and B are the fuzz)- subset of e and e The fuzzy subset of all output parameters consist of a independent fq ubset. The subordination function of combination output is: y,,,,, x) = max{u, x),u, x)} (4) Where: C, is the fuzzy subset ofAu 2.2 HGA (Hybrid Genetic Algorithm) GA is necessary to apply optimal fuzzy set for control [61 The evolution process happens on the chromosomes. which represent the values of 4 nd 8 nput subordination fimction of the fuzzy subset. Its actual value codes are: c = {a,., Lt :... a,,,. p a<., 4 ...a;,,, 8: a,,, p. .:... a ., Where: u',nand/3en re nput subordination function of the u'4m?dD2n re input subordination ruuction of the a undpun re input subordination function of the fuzzy subset of error nrh fuzzy subset of the changes of error nm fuzzy subset of error h f Azr ; Evolution and genetic Operation: Father 1 ather2 Son Fig 6 Operahon 1 one point exchange 1456  Father1 zyxwvutsrq Father2 [   Fig.7 Operation 2 zyxwvutsrqp wo point exchange , , Natural evolution (or the evolution happens in other place) isn't a fixed process. In fact, natural evolution pmcess zyxwvu s a process to compete for resources, where those units that have better life ability are easier to zyxwvutsrq mvive and spread their gene material. Steady-state reproduction method is applied, where only one chromosome is reproduced in every generation zyxwvutsrqp ne point exchange1']: A new generation is born when father 1 is replaced by father 2 in a randomly selected point (Fig.6). Shift lift rids Shift right side Fig.8 Operation 3- Subset shift Shifl liR comer Shift rightcoma .. . .. . ., , Fig.9 O&ation44orner Shift ,, TNO point exchange I: It is similar lo one point exchange; exceptltl~al wo division points are randomly selected. A new generation is born after the segment behveen the two division points of father 1 is replaced by father 2 (Fig.7). Subset Shift: randomly to left side (or right side)..It shif in parallel to keep the subset shape, zyxwvutsr s FigX. Comer Shift: The operation shift randomly the sclccted fuzzy subset L steps. L is also selected.randomly to left corner (or right comer), zyxwvutsr s Fig.9. Subset Expansion: The operation shift mndonlly L steps. L is also selected . Similar to operation 3, but nmst sld o both &&tion simultaneously (Fig. 10). 8. ,,. .. Fig.10 Operation 5 Expansion of sub set 2.3 System Performance Adaptability evaluation: The quality of fuzzy subset used in control is chging. The performance of control systcm varies with the adaptability standard of genetic algoritlun For most dynamic control system, over shoot, stable error,'and rcaction time a? often considcrcd, To reflect the benefit of fuzzy . control subset, three parameten. are. mainly considered in the adaptahility evaluation: 1) over shoot' ewe, 2) peak time value zyxwv   ; (3) Stable average square ermr e_. So, defined as: F =. qe + a2tp, a3e:.~ 2 (6) . Where zyxwv i s the parameter weight. Colony size is about 2 c9? .. .. ~ 3. EXPERIMENT ESULT 3.1 GA performunce To prove the.optimization ability of Genetic Algorithm. 50 generations are built. The average'adaptability of every new generation is closer to'the value tk heir.father generation. ,This trend proves that, hc latest generation bas the'best performance. The best cbromosome:is: C=[12 17 20 21 5 46 33 86 65 71.60 78 77 87 18 21~17 2 7 73 47 91 6 79 19 77 76 92 17 28 11- 5X 11 78 46 64 5 86 11 98 98 991 3.2 Closed loop performance Two groups of experiment are ananged. One group sets PC302as 0.237MP, the experiment result proved that the actual value of PC302 can recover to 0.237MP even when there exist a great change in input material of tower 2, the fluctuation is lees than 5%. Another group applied square wavefor the set value of PC302 wave amplitude is 0.03Mp, maximum value is 0.207MP, minimum value is 0.237MPa, experiment result proved that the actual value of PC302 can follow up its set value quickly and accurately. .. 1457  4. zyxwvutsrqp ONCLUSION zyxwvuts xperiment& result proves that, even through there is strong interference, he algorithm still can ensure the actual pressure value to fellow the given pressure value quickly and steadily. GA is proved to be successful to optimize zyxwvu uzzy logic control. In fact, Gcnetic Fuzzy Algorithm not zyxwvu nly zyxwvutsr an control pressure of distilling tower, but also solve other optimization problem existing in other applications I. Reference: [I] Gang zyxwvutsrq iong The Architecture, Intelligent Tools of CIPS and its Application Doetoral Dissenation of Shanghai Iiao Tong Univeniy. 1996.10 Karr C.L. and Stxiley D.A. Fuzzy logic and genetic algorithms in time-varying control problems. ProcNAFIPS-91:28S-290 Meredith D.L., Kma K.K. and Karr C.L. The use of genetic algorithms in the design of zyxwvutsrqpon uuy logic controllers .PROC. WNNAlND 91.695-702 Park U,,. Kandel A. and Langholz U. Oendic-Based New Furzy Reasoning Mod& with Application to fuuy control. IEEE Tmr. zyxwvu yst. Man and Cybernetics. 1994.VolZ4,No.l:39-27 [SI Davis L. Handbook of Genetic Algorithms. Van Nostrand Reinhold.1991 [6] Goldbsrg D.E. Gendic Algorithm iii Search Optimization, aid Machine Learning. Addison-Wesley. 1989 [7l Holland J.H. Adaptatim in Natural and Artificial Systemr. Univ-ily of Michigan Press 1975 [SI Fshehm LJ.,Caruana R.A. and Schaffer J.D.. Biases in the emover landscape. Proc. 3rd Int Conf. Genetic Algorithm .1989:10 19 Schaffer J.D.. Caruana R.A. and Eshelman L.J. et al.. A study of control parametar affecting online performance of genetic algorithms for funetion optimization. Roc 3rd Int. Cod. Genetic Algorithms. 198931-60 121 [3] 141 [9] Acknowledgment: 'This research is partly supported by several resources. 'The project supported by Academy of Finland: Integrated Modeling and Overall Optimization of Strategic Automation System used in Modem Pulp Paper Industry . The project of Process Integration Program supported Finnish TEKES: Novel Information Integration Technologies WIT) for Strategic Automation System (SAS) used in Modem Pulp Paper Industry . NEPPI Project of TEKES: Networked Enterprise for Pulp Paper Industry. VPPE-project supported by China-Finland government cooperation research program 2000-02: Modeling for Virtual Enterprise and E-logistics Supporting Technologies, Chinese partner: Academic Prof. Cheng Wu, CIMS ERC. Tsinghua University, China. Fuzzy Logic System ............................................................................. Fig.] The structure of Genetic Fuzzy Logic Controller (GFLC) Fuzzy Fuzzy Inference Maclune = zyxwvuts e, zyxwvut   Inpul t t t Input subordination Fuzzy Rule Base Output subordination Function Function Fig.2 A Fuzzy Logic System 1458
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