EXPERT CONTROL BASED ON NEURAL NETWORKS FOR CONTROLLING GREENHOUSE ENVIRONMENT

Transcription

1 EXPERT CONTROL BASED ON NEURAL NETWORKS FOR CONTROLLING GREENHOUSE ENVIRONMENT Le Du Bejng Insttute of Technology, Bejng, , Chna Abstract: Keyords: Dependng upon the nonlnear feature beteen neural unts n artfcal neural netorks (ANN), artfcal neural netork as used to develop a model for greenhouse nsde ar temperature management. Data as collected and processed for tranng and smulaton of temperature model. The desgn of the netork structure and transfer functon as also dscussed. Because heurstc logc of greenhouse envronment exsts, a smple ANN controller does not perfect complex task, the algorthm of ANN th heurstc logc s developed. Expermental result ndcated that the performance of the ne control scheme meet the requrement of greenhouse envronment control. complex system, neural netork, greenhouse envronment 1. INTRODUCTION The greenhouse envronment belongs to the complex system (G. van Straten et al., 000; N. Sgrms et al.,000; M.Trgu et al., 000), t s dffcult to control due to hgh nonlnear, and uncertan. System confguraton s shon n Fg. 1 Outsde Sensors Desgn Controller Set pont Controller Insde Sensors Actuators Fg. 1 Greenhouse Envronment Control Archtecture

2 temperature ( ) Le Du The man purpose of greenhouse s to mprove the envronmental condtons n hch plants are groth.the am of greenhouse envronmental control s to provde means to further mprove these condtons n order to optmze the plant producton process.the greenhouse clmate s nfluenced by many factors, for example nsde ar temperature, the actuators, outsde eather and crop. Insde ar temperature s the most mportant factor affectng crop groth n greenhouse clmate. Temperature exerted sgnfcant effects on photosynthess. The net photosynthetc rate (Pn ) vared n day tme, t as double peak curve,there as obvous mdday depresson of photosynthess, and 1-33 as the sutable temperature for photosynthess n summer. Methods amed at effcently controllng the greenhouse temperature must take these nfluences nto account, and that s acheved by the use of models. Ths model s of captal nterests n the study of the dynamcs of the greenhouse temperature under dfferent clmate condtons, amed at producng nsde temperature regularty hch helps to avod extreme stuatons (hgh temperature or lo temperature, etc.) and to optmze crop producton by achevng adequate temperature ntegrals hle reducng polluton and energy consumpton tme (h) Fg.. Chart of varable temperature In ths control system for plant groth, the key problems nclude control system nput so called set pont and control algorthm. The tradtonal set pont s shon n Fg.. Because set pont changes abruptly, that astes a large of energy sources. So e must seek a method to transform a segment of steps chart nto a smooth cure for savng energy. If e control a varable temperature, the tradtonal method s to use a controller (PID) to generate control output t). Hoever, the control t) relates many actuators, so optmal method s used to decde hat actuator to act. Snce the tradtonal method s dffcult to control ths system, Artfcal neural netorks and symbolc descrpton methods are ntroduced. Artfcal neural netorks are dely appled n greenhouse envronmental control to perform some type of non-lnear system mappng. In the felds of dentfcaton and modelng of non-lnear systems ther unversal

3 Expert Control Based On Neural Netorks For Controllng Greenhouse Envronment 3 approxmaton property s exploted. Prevous ork on ths ANN modelng task reled n an nput output model structure selected from (Cunha et al.,1996) n the context of dynamc temperature models dentfcatons. Ths structure as selected by means of the (Akake 1974) nformaton crteron here several hypothess ere tested and the best one chosen. The applcaton of RBF NN to greenhouse nsde ar temperature modelng has been nvestgated (Ferelra, P.M. Ruano, A.E., 00). These models alays arse lke non-stablty, non-regularty, hch result n the complete loss of energy. Ths paper uses artfcal neural netork theory and method to control the range of temperature change and dscuss the ANN model th heurstc logc.. ALGORITHM DESIGN OF NEURAL NETWORKS DIRECT SELF-TUNING CONTROL FOR GREENHOUSES WITH HEURISTIC LOGIC Drect self-tunng control th an artfcal neural netork s shon n Fg.3 hch ncludes three basc secton: (1) Feedback loop conssts of self-tunng controller and controlled plant. () The parameters of the controller are obtaned by desgnng artfcal neural netorks dentfcaton and controller. (3) Heurstc logc decson hat actuators are operated. It s obvous that the key technque of self-tunng controller s ho to desgn artfcal neural netorks dentfcaton and controller ell and gve approprate heurstc logc. Desgn Controller ANNID r ANN Controller 1 u Ventlato b 1 Heat b Greenhouse Envronment y n Shade b n Fg. 3. Neural netorks ndrect self-tunng control Consder the nonlnear system, n b, b, 1, b are the valve value of n actuators. 1,,, are the eght value of n actuators, n

4 4 Le Du k 1) g[,, k n 1;,, k m 1)] (1),, k n 1);,, k m 1)] Where functon. u, y are the nput and output of the system., and [, g are nonzero If g [, are knon functons, control algorthm of the controller accordng to certan equvalence prncple s as follo: g[ r( k 1) () u ( The control system attempts to make the output y ( match the reference nput r (. If g [, [ are unknon functons, the artfcal neural netorks dentfcaton s traned on lne so that g [, [ approxmate controlled plant. Usng Ng [, substtute for g [,, control algorthm of the controller accordng to certan equvalence prncple s as follo: Ng[ r( k 1) (3) u ( here Ng [, s nonlnear dynamc the artfcal neural netorks respectvely..1 The artfcal neural netorks dentfcaton For smple problem, one order system s governed by the follong equaton: k 1) g[ (4) The artfcal neural netorks dentfcaton s shon n Fg. 3. The dentfcaton conssts of to three-lays nonlnear DTNN, here number of order of Ng [, s equal to number of order of g [,. The nput of the artfcal neural netorks s { y (, }, and the output s as follos: yˆ ( k 1) Ng[ W ( V ( (5) here W, V are to neural netorks eghts hch are gven: W ( [, (, (,, ( 0 1 p V ( [ v, (, (,, ( 0 v1 v v p th p s hdden layer node number Ng[0, ]; v 0, ] 0 W 0 V

5 Expert Control Based On Neural Netorks For Controllng Greenhouse Envronment 5 yˆ ( k 1) V0 W0 Fg.4. The artfcal neural netorks dentfcaton In Fg. 4,L s lnear node, and H s nonlnear node. substtute (4) nto (5 ),then the output of the control system: Ng[ r( k 1) (6) y ( k 1) g[ f Ng [ g[ and N, y[ k] r[ k] By mnmzng the quadratc performance crteron, get eghts tunng process thought tranng the artfcal neural netorks dentfcaton 1 1 (7) E ( [ r( k 1) k 1)] e ( k 1) W ( k 1) W ( W ( ; V ( k 1) V ( V ( Usng Bp learnng algorthm(ferelra et al., 00; Tetsuo et al., 000) E( ( ; ( Substtute (7) nto ( 8), e get: Ng[ y ( E( v ( v v ( Ng[ W ( ( e( k 1 V ( ( (8) In the some ay, e get:

6 6 Le Du v V ( ( v e( k 1) V ( ( v (9) here [ s unknon and sgn s knon, hch s marked as sgn{ [ }.After sgn{ } s substtuted to [ of equaton (9) and (10), ( k 1), ( k 1) are gven by v sgn{ } Ng[ W ( ( k 1) ( e( k 1) W ( ( (10) v sgn{ } V ( ( k 1) v ( e( k 1) v V ( ( v (11). Control algorthm th heurstc logc As stated above, gven control u nvolves many actuators operaton, so control output u of fst step does not take acton on any actuator, control output u must accomplsh follong mappng to yeld real control output for actuator. 1 1 r1 ( t) b ur u (1) Where,, 1, n and b, b, 1, bn result from heurstc logc, so the control algorthm possesses ntellgence. 3. EXPERIMENTAL RESULTS OF CONTROL In ths part, the effectveness of the proposed control method ll be demonstrated by a case study for nter heatng system, many actuators are forbdden due to energy conservaton, the actuator of heatng system operates only. In ths case, 1, and b s determned by pror knoledge. Fg 5 s expermental chart measured by the control system n Feb. 9, 004.

7 19:40 0:58 :17 3:36 0:54 :13 3:3 4:50 6:09 7:8 8:47 10:11 11:30 1:49 14:08 15:6 16:46 temperature( ) Expert Control Based On Neural Netorks For Controllng Greenhouse Envronment set pont real value tme (H:M) Fg.5. Comparson chart of temperature beteen set pont and real value From Fg 5, t s seen that the chart of real control value tracts that of set pont and lght ntensty ncreases n Fg 5 gradually at 7 to 16, provded energy for greenhouse decrease gradually. Energy chart omtted. 4. CONCLUSION The presented the algorthm of ANN th heurstc logc proved to be very effectve n meetng formal requrements for greenhouse control such as set pont and trackng set pont. ANN modelng set pont s feasble. The key technque for greenhouse envronment control uses ANN and heurstc logc reasonably to create a good algorthm. The proposed control algorthm s currently mplemented n based CAN bus control system for greenhouse envronment control. REFERENCES G. van Straten, H. Challa and F. Bualda Toards user accepted optmal control of greenhouse clmate. Comp.Electroncs Agrc, vol. 6. 3, (000) 1-38 N. Sgrms, A. Anastasou and N. Rerras Energy savng n greenhouses usng temperature ntegraton: a smulaton survey. Comp.Electroncs Agrc, vol.6. 3, (000) M.Trgu et al.a strategy for greenhouse clmate control. part I: model development J. Agrc. Engng. Res. Vol , (000) Cunha,J.Boaventura, A.E. Ruano and E.A. Farra. Dynamc temperature models of a sol-less greenhouse. In: Proceedngs of the nd Portuguese Conference on Automatc ontrol.vol.1. Portuguese Assocaton of Automatc Control. Porto, Portugal. (1996) Akake,H.A ne look at the statstcal model dentfcaton. IEEE transactons on Automatc Control vol.19, (1974) Ferelra, P.M. Ruano, A.E. Choce of RBF model structure for predctng greenhouse nsde ar temperature[c]. 15th Trennal World Confress, Barcelona, Span. 00 IFAC.

8 8 Le Du Tetsuo Mormoto and Yasush Hashmoto. An ntellgent control for greenhouse automaton, orented by the concepts of SPA and SFA an applcaton to a post-harvest process. Comp. Electroncs Agrc, vol. 9., (000)3-0