Journal of Chemical and Pharmaceutical Research, 2014, 6(5): Research Article

Transcription

1 Avalable onlne Journal of Chemcal and Pharmaceutcal Research, 014, 6(5): Research Artcle ISSN : CODEN(USA) : JCPRC5 Multple mode control based on VAV ar condtonng system research Jeja L 1, Xaoyu Sun 1*, Peng Yang 1, Lang Zhou 1 and Abdol Ghaffar Ebad 1 School of Informaton and Control Engneerng, Shenyang Janzhu Unversty, Laonng, Chna Department of Bology, Payame Noor Unversty, I.R. of IRAN ABSTRACT In vew of the characterstcs of varable ar volume (VAV) ar condtonng system whch s multple varable, nonlnear, large delay and tme-varyng. In the control of ar condtonng f we only use one knd of tradtonal ntellgent control method to deal wth the factors such as dsturbance and uncertanty can not get good control effect. In ths paper, the multple mode control method s ntroduced n VAV ar condtonng system. Ths method sets up two knds of control modes, chooses dfferent controlled objects under dfferent dsturbance n order to acheve rapd adjustment and characterstc of energy savng. It combnes fuzzy neural network and predctve control to establsh a neural network predcton model, and uses the dfference between the predctve output and the actual output to adjust the parameters of the predctor onlne. The results show that the control effect of the multple mode control system s better than the tradtonal neural network predctve control system. Multple mode control method has the characterstcs of strong robustness, hgh control precson, strong adaptve ablty, safe, relable and energy savng. The system has wde applcaton prospect. Key words: VAV ar condtonng system; Multple mode control; Fuzzy neural network; Predctve control; INTRODUCTION VAV ar condtonng system s a full ar system, t controls the temperature n one area of the buldng of an ar condtonng system by changng the supply ar (also can adjust supply ar temperature), the system can adjust the ar condtonng ar output automatcally accordng to the requrements of the changng ar condtonng load and ndoor parameters, t could satsfy the requrement of ndoor personnel comfort and save much of energy at the same tme. Due to the characterstcs of hgh effcency, energy savng, easy to reorganzaton and expanson the research of the VAV ar condtonng system has become a focus n research of ntellgent buldng feld. However, because of the VAV ar condtonng system has the characterstcs of multple varable, nonlnear, large delay and tme-varyng, the tradtonal control method has been unable to acheve good control effect. The method of multple mode control sets up an ar condtonng control system,whch combnes the control of ar volume sze, supply ar temperature, ar supply duct statc pressure regulatng. Through the central controller ths method can selectvely control dfferent controlled object. And take advantage of the characterstcs of neural network can be a very good approxmaton of nonlnear system. Establshng a reference model on-lne, completng the dynamc characterstcs of a system dentfcaton, mprovng the control effect. II. THE WORKING PRINCIPLE OF VARIABLE AIR VOLUME AIR CONDITIONING SYSTEM Varable ar volume ar condtonng system s manly composed of four parts, for ar handlng and transportaton equpment, duct system, automatc control system and VAV termnal devce (VAV box). The workng prncple of VAV ar condtonng system s: the ar condtonng system calculates the collected data from the montorng devce,after comparng wth the set value,t wll calculate the openng of each end (termnal )of the ar valve should 1683

2 Jeja L et al J. Chem. Pharm. Res., 014, 6(5): be, through the termnal devce, adjust the room temperature to the default values. So how to make the ar condtonng system acheve the control goal fast and stable s the pvotal ssue of whether the ar condtonng system control performance s good or not. To meet people's requrement of ar qualty and energy savng effect we need to come up wth a better control strategy. III. THE STRUCTURE OF THE CONTROL SYSTEM DESIGN By changng the ar volume to the ar condtonng room VAV ar condtonng system adjust the ndoor temperature to room temperature set pont, compared wth the constant ar volume ar condtonng system, most of tme of the VAV ar condtonng system s workng under partal load and low load condton. However, when large amount of nterference n the room, just by changng the delvery room temperature to acheve the set value, wll make the regulatng tme of the system s too long, or ncrease the ar volume nput at the same tme to mprove the quckness of the ar condtonng system reachng set temperature, ths method can not only ncrease the energy consumpton of the system, also can affect the ndoor envronment comfort badly. So, t s the most energy-effcent mode by adjustng the ar volume sze to adjust the room temperature reaches the temperature set pont when the error between the actual temperature and set temperature wthn a certan range, however, when the error value beyond a certan range,just uses the adjustable ar volume change to acheve the temperature set pont wll ncrease the system energy consumpton, reduce the comfort of the room. So when the dsturbance n the room s dfferent usng dfferent control methods. The control system structure dagram s shown n fgure 1. Two knds of control mode are presented n ths paper, the dfference between them s dfferent output and dfferent control objects. But fuzzy controller 1 and fuzzy controller have the same algorthm and nput varable, the four nputs of the fuzzy neural network are the error e 1 between the room temperature set pont and the actual temperature value, the error e between the neural network predcton output and room temperature set pont, the error e 4 of return ar temperature and the ar duct statc pressure values. But the two output varables apply to dfferent controlled objects, when the error e 1 s greater than a certan range,changes the apply ar volume and ar supply temperature at the same tme, means that the method of the fuzzy neural network controller output varables u 1 of fuzzy neural network controller 1 s two-dmensonal. They control the supply ar duct heatng devce nsde e 3 e u u 1 Z 1 e 1 u Fgure1.the structure of the control system the motor and the ar valve of the supply fan to adjust ar valve, respectvely. When the error between the room temperature set pont and the actual temperature does not exceed a certan range the system stll adopts varable ar volume adjustment method, namely, the output varables u of the fuzzy neural network controller s one-dmensonal, controls the ar valve of the supply fan. As the room settng error e1 changes, choose dfferent control method to realze the room temperature adjustment, reduce system energy consumpton as well as mproved the comfort of the room. Now set the crtcal value of e1 s E k. When the value of the error e1 s greater than E k, the fuzzy neural network controller 1 was open, assumng ths control method s model 1. When the error e 1 value between 0 and E k,the fuzzy neural network controller was open, assumng ths control method s model.when choosng dfferent fuzzy neural network controller, the nput u of predctor also changed accordngly, u s the output of the fuzzy neural network controller. 1684

3 Jeja L et al J. Chem. Pharm. Res., 014, 6(5): IV. THE DESIGN OF FUZZY NEURAL NETWORK CONTROLLER AND NEURAL NETWORK PREDICTOR A. The Desgn of Fuzzy Neural Network Controller Ths paper proposed the ar condtonng system control part s composed of controller and predctor, the controller s composed of fuzzy neural network controller 1 and fuzzy neural network controller, they are parallel. By judgng the system selects one of control modes and passes ts output to the actuators. The fuzzy neural network controller structure s shown n fgure.there are four layers n the fuzzy neural network, they are nput layer, component layer, rule layer and output layer. They perform assgnng, fuzzfcaton, blurred fuzzy calculaton and the calculaton of the defuzzfcaton. The controller network parameters of the model of are the component value m of each neuron n the component layer, the wdth δ and the weght W between rule layer and output layer. Other connecton weghts of each part are l. In ths paper s defned as the nput of the neurons, G s the output of the neuron, The top rght corner mark sad the layer of the neurons n, The lower rght corner mark sad the layer order number of the neurons n. The frst layer s nput layer. In ths layer the external nput of neurons s passed to the next layer drectly.the frst layer has the effect of transton and allocaton n sgnal transmsson, namely Z = 1) m x m ( (1) In the formula, xm s the mth component of the external nput,n ths paper, xm s the mth nput varable of the controller. The second layer s component layer. Through the component functon the neuron blurs the nput data, namely g 1 g g 3 g R x 1 x... x 3 Fgure.the structure of the fuzzy neural network controller Z () m (u m ) (x m ) = exp( ) = exp( ) () In the formula, () () () m m m m ( δm ) ( δm ) u Z are the th neuron s nput and output when the mth nput s blurred; () () m m m δ ) are the component value and the wdth. The network of ths paper dvdes each network nput nto fve, they are negatve bg, negatve, zero, small and the board. Therefore, usng fve Gaussan functon respectvely, namely (x m m) f (x) = exp( ) (3) δ () m ( m The thrd layer s the rule layer. And the blurred result of the second layer, the fuzzy rules s: set x s a M dmensonal vector, component language number value on the unverse of dscourse of each component are I 1,I,...,I M; set Z s a N dmensonal vector, component language number value on the unverse of dscourse of each component are J 1,J,...,J N. Usng the network operaton realze the nference model, n the form of the f f-then fuzzy rules. In the frst layer usng m nput neuron respectvely corresponds to the each component of x varables n the fuzzy rules. On the second layer, respectvely, usng I 1, I,...,I M correspondng to the subentry language values A 11,A 1,...,A MIM of the f-then fuzzy rules. 1685

4 Jeja L et al J. Chem. Pharm. Res., 014, 6(5): In the thrd layer usng product operaton mplement of nference rules "and". For ts expresson s Z Kr (3) (3) r ur k k = 1 = (4) In the formula, Kr s the number of all the nput varables of the rth neurons n the thrd layer The fourth layer s output layer, use the method of weghted square to defuzzy, namely g = Z = R (4) = 1 n n R = 1 w y n y (3) (3) (5) In the formula, W n s the weght of output layer; Y s the output of the sub-network of the th component fuzzy neural network. B. The Desgn of the Predctor Predctor n ths paper s recursve wavelet neural network, ncludng the nput layer, hdden layer, structure layer and output layer, the nput of the neuron s the output of the neuron n hdden layer, output of structure layer and nput layer are the nput of the hdden layer. The structure layer neurons s also a knd of memory unts, the storage of the hdden layer neurons on the output of the step, the memory let that network has good dynamc performance. There are three vectors n nput layer of the network, they are the error e 3 of control object actual output and the output of predcton, the actual output value of y the moment before, output value of the fuzzy neural network controller u(u s u 1 or u, when fuzzy neural network 1 s opened u s u 1,u 1 s a two-dmensonal varables, when fuzzy neural network s opened u s u, u s one-dmensonal varable ),one output layer nodes and no matter choose whch knds of control mode hdden layer and the structural unt has fve nodes. Network output t o for the room temperature forecasts the output value of the next moment, the network nput o(k) s a 3 dmensonal vector, output x (k) and hdden layer structure unt output xc (k) of 5 dmensonal vector, network output t o (k) as 1 dmensonal vector, connecton weght W 1 s 5 x 5 dmensonal matrx, W s 5 x 3 dmensonal matrx, W 3 matrx s 1 x 5 dmensonal. Because the wavelet neural network s ntroduced nto the scale factor and shft factor, so to ncrease the flexblty of the network, can be more effectvely and make the tranng process approxmaton accuracy of target value, tranng effect s more deal. The mathematcal model of wavelet Elman neural network as follows: x (k) = α x (k 1) + x(k 1) (6) c c h(k) b (k) x(k) ψ ( ) a (k) = (7) 3 g(w (k) x(k)) y (k) = (8) 1 In the formula: h (k) = W (k) x (k) + W (k) u(k) (9) c ψ (.) s the wavelet functon. Ths artcle takes the morlet wavelet, set matrx, b s the wavelet translaton matrx. set X h(k) b (k) a (k) a s the wavelet scale coeffcent = (10) For the wavelet translaton matrx s ψ (X) X = cos(1.75 X) e (11) g( ) s the transfer functon for the output layer. V. THE SIMULATION Snce each parameter of ar condtonng room all nfluenced of such factors as outdoor temperature, ndoor equpment, lghtng and heat flow, the ar condtonng room s a complex thermal system. Therefore, t s dffcult to 1686

5 Jeja L et al J. Chem. Pharm. Res., 014, 6(5): use a precse mathematcal model to descrbe. Assumng the whole ar condtonng room as a sngle object, gnore other objects' accumulaton and ar flow, the room temperature s regard as unform dstrbuton. Assumng that the transfer functon of the ar condtonng room s: τ s ke G(s) =, T1= 1,T= 5,k = 18, (T1s + 1)(T s+ 1) 18 1s τ = 1,So G(s) = e 60s + 17s + 1 Because as the error value E between the room temperature set pont and the actual temperature changes, room control system wll choose a dfferent pattern mplementaton room temperature control, but acheve the crtcal value Ek needs experment many tmes, therefore, ths paper only select a smaller temperature dfference value and a larger temperature dfference value as representatves of the two control methods to compare n the Matlab smulaton software. Control mode 1:The control system select control mode 1 when the nterference of the room s small. At ths tme, control object s the ar valve and heater motor. Now assume that the current temperature of room s 18, ar condtonng room settng temperature value s 5.Respectvely, n the Matlab smulaton software usng multvarate ar condtonng control method and the tradtonal parallel neural network predctve control method for smulaton analyss, the control effect contrast dagram as shown n fgure 4. Fgure 4.smulaton dagram 1 It can be seen from the above, when parallel multvarable ar condtonng control system s choosed, response tme s short, overshoot volume s about 4%, settlement tme s about 00s, the steady state error s 0.04%, the tradtonal neural network predctve control system response tme s a bt long, overshoot volume s about 6%, settlement tme s about 80s, the steady state error of 0.4%,so when the room nterference amount s larger, or t has a larger error between the actual temperature and the temperature set pont, control effect of multple mode control s better than the tradtonal neural network predctve control system. Compared wth tradtonal VAV control system, parallel multvarate ar condtonng control system has the characterstcs of hgher power, small amount of overshoot, small steady-state error, not only save energy and mprove the ndoor envronment of comfort. Control mode :The control system select control mode when the nterference of the room s bg, at ths tme, control object s ar valve. Now assume a current temperature of room s 1, ar condtonng room settng temperature value s 5. In the Matlab smulaton software usng multvarate ar condtonng control method and the tradtonal parallel neural network predctve control method for smulaton analyss, the control effect contrast dagram as shown n fgure

6 Jeja L et al J. Chem. Pharm. Res., 014, 6(5): Fgure 5.smulaton dagram It can be seen from the dagram above, ths tme the parallel varable ar condtonng control system and the tradtonal neural network predctve control system s response tme, medaton, the steady-state error s bascally the same, parallel multvarate ar condtonng control system overshoot volume s about 4%, the tradtonal neural network predctve control system overshoot volume s about 5.6%, the dfference s not bg. So when the error between the actual temperature and the temperature set pont s small, select parallel multvarate ar condtonng control system and tradtonal VAV ar condtonng control system the dstncton both can acheve the result of energy savng. CONCLUSION In ths paper, the multple mode control method s ntroduced to the VAV control system, ths method sets up two knds of control modes, t chooses dfferent control objects under dfferent dsturbance n order to acheve rapd adjustment and the characterstcs of energy savng, The controller adopts fuzzy neural network, and the predctor adopts wavelet neural network, combnaton of them establshes a neural network predcton model of the object onlne, and uses the dfference of the predctve output and the actual output to adjust the parameters of the predctor onlne.. The results show that, the control effect of the multple mode control ar condtonng system s much better than the tradtonal neural network predctve control system. Multple mode control method has the characterstcs of strong robustness, hgh control precson, the adaptve ablty s strong, safe, relable and energy savng, t has wde applcaton prospect. Acknowledgment Ths work was supported by the Natonal Natural Scence Foundaton of Chna under Grant No REFERENCES [1] Dong. J.C, Industral Instrumentaton & Automaton.1:19-,001 [] Zhang.d.b,Huang.k.x, Informaton and Control.4(5): ,013 [3] Jn.X.Q,Zhou.x.x,Wang.C.W, Journal of Shangha Jaotong Unversty,34(4):507-53,000 [4] Fan.D.X,Varable ar volume (VAV) ar condtonng system of Smulnk modelng and smulaton. Harbn Industral Unversty,007 [5] Guo.X.Y, Journal of shenyang unversty of technology,35(1):99-103,013 [6] Wu.Z.S,VAV BOX control algorthm and smulaton South Chna Unversty of Technology,01 [7] Song.X.Y,Xu.H.F,Sun.H.L, Chnese Journal of Computers,36(8): ,013 [8] H.S. Xu and X.L. Hou, Journal of Shenyang Janzhu Unversty (Natural Scence), Vol.7,No.3, pp ,