Research Article Intelligent Control for USV Based on Improved Elman Neural Network with TSK Fuzzy

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1 Advances n Artfcal Intellgence, Artcle ID , 9 pages Research Artcle Intellgent Cntrl fr USV Based n Imprved Elman Neural Netwrk wth TSK Fuzzy Shang-Jen Chuang, Chung-Hsng Chen, Chh-Mng Hng, and Guan-Yu Chen Department f Electrnc Cmmuncatn Engneerng, Natnal Kahsung Marne Unversty, Kahsung 81157, Tawan Crrespndence shuld be addressed t Chh-Mng Hng; d @student.nsysu.edu.tw Receved 7 January 2014; Revsed 7 Aprl 2014; Accepted 22 Aprl 2014; Publshed 18 May 2014 Academc Edtr: AntónDuradPereraCrrea Cpyrght 2014 Shang-Jen Chuang et al. Ths s an pen access artcle dstrbuted under the Creatve Cmmns Attrbutn Lcense, whch permts unrestrcted use, dstrbutn, and reprductn n any medum, prvded the rgnal wrk s prperly cted. In recent years, based n the rsng f glbal persnal safety demand and human resurce cst cnsderatns, develpment f unmanned vehcles t replace manpwer requrement t perfrm hgh-rsk peratns s ncreasng. In rder t acqure useful resurces under the marne envrnment, a large bat as an unmanned surface vehcle (USV) was mplemented. The USV s equpped wth autmatc navgatn features and a cmplete substtute artfcal manpulatn. Ths USV system fr explrng the marne envrnment has mre carryng capacty and that measurement system can als be self-desgned thrugh a mdular apprach n accrdance wth the needs fr varus types f envrnmental cndtns. The nvestgatn wrk becmes mre flexble. A catamaran hull s adpted as autmatc navgatn test wth CmpactRIO embedded system. Thrugh GPS and drectn sensr we nt nly can knw the current lcatn f the bat, but als can calculate the dstance wth a predetermned pstn and the angle dfference mmedately. In ths paper, the desgn f autmatc navgatn s calculated n accrdance wth mprved Elman neural netwrk (ENN) algrthms. Takag-Sugen-Kang (TSK) fuzzy and mprved ENN cntrl are appled t adust requred pwer and steerng, whch allws the hull t mve straght frward t a predetermned target pstn. The rute wll be free frm utsde nfluence and realze autmatc navgatn purpse. 1. Intrductn Durng WWII, there were reprted recrds f unmanned vessels fr reducng the damages f vessels as well as nures and fatalty f human. Usng small trpeds r larger-szed unmanned shps fr cllectng nfrmatn [1], glbal pstnng system (GPS) brngs hgh effcency thrugh the use f lw-cst, unmanned desgn. There s n need fr the cncern f plt s safety n usng unmanned vehcles fr marne envrnmental survey. Vessels fr marne envrnmental survey are usually equpped wth USV system. It allws heaver ladng capacty. In addtn, the desgn f vessels can be mdularzed. There s better flexblty fr adustment accrdng t the needs frm varus types f envrnments and nvestgatns. The effectve cntrl range f many USV systems vares frm 50 meters t 30 klmeters [2, 3], manly restraned by the wreless transcever mdules. In rder t ncrease the effectve wrkng range f USV, a desgn f unmanned vessels wuld be necessary. T allw USV systems t be autnavgatng and t replace manual peratn cmpletely, the autnavgatn system s the mst needed task fr every unmanned carrer. The Elman neural netwrk (ENN) was frst prpsed fr speech prcessng. Generally, the ENN can be cnsdered as a specal knd f feed-frward neural netwrk wth addtnal memry neurns and lcal feedback. Because f the cntext neurns and lcal recurrent cnnectns between the cntext layer and the hdden layer, t has certan dynamc advantages ver statc neural netwrk, such as multlayer perceptrns and radal-bass functn netwrks. It als makes ENN very sutable t be appled n the neurcntrl feld. Hwever, the typcal ENN cannt apprxmate hgh-rder dynamc systems clsely, and ts cnvergence speed s usually slw and nt sutable fr tme-crtcal applcatns. Several knds f mdfed ENNs were prpsed t vercme such ssues

2 2 Advances n Artfcal Intellgence Server PC USV W-F AP GPS Drectn sensr crio Pwer mtr Steerng servmtr Fgure 1: System archtecture. t mprve the dynamc characterstcs and cnvergence speed f the rgnal ENN [4, 5]. Cmpared wth BP neural netwrk, ENN has many advantages: faster cnvergence speed, less tranng teratn, strnger rbustness, n lcal mnmum, and s frth. On the ther hand, the methd f fuzzy nference prpsed by Sugen and Kang [6], whch s knwn as the Takag-Sugen-Kang (TSK) mdel n fuzzy systems lterature, has been ne f the mar tpcs n theretcal studes and practcal applcatns f fuzzy mdelng andcntrl.thebascdeafthsmethdstdecmpse the nput space nt fuzzy regns and t apprxmate the system n every regn by a smple mdel. The verall fuzzy mdel s thus cnsdered as a cmbnatn f ntercnnected subsystems wth smpler mdels. 2. Research Methds The desgned vehcle s verall system archtecture ncludes a hgh precsn prgrammable cntrller-crio as man cre, a glbal satellte pstnng recever develpng mdule (GPS recever mdule), a drectn sensr, a steerng servmtr, and pwer mtr, whch are all shwn n Fgure Unmanned Surface Vehcle. The man bdy f USV n ths paper s a catamaran-type bat whch was desgned and bult by students and teacher f Department f Naval Archtecture and Ocean Engneerng, Natnal Kahsung Marne Unversty. Ths catamaran s 5.27 meters lng, 1.74 meters wde, and 1.17 meters hgh, and the electrc mtr t drve the pwer can carry 5 6passengers. Usng a catamaran desgn as an USV has the fllwng man features: (1) sea wave balance perfrmance s better; (2) lw-speed navgatn and rtatn f the catamaran are better; (3) t has a flat usable space and s easer t allcate requred nstruments t cnduct measurement wrk. The desgned USV prvdes mre flexble space t nstall varus equpment (e.g., water qualty mntrng meter, fsh fnder machne, anemmeter, wnd drectn sensr mdule, etc.), t establsh marne envrnmental data cllectn. A large slar panel nstalled n tp f the hull can strengthen salng range. Fgure 2: Glbal pstnng system [7]. Fgure 3: GPS recever Glbal Pstnng System. Glbal pstnng system (GPS) s a cmbnatn f satelltes and wreless cmmuncatn technlgy as shwn n Fgure 2. GPS s a glbalzatn and all-purpse system whch has many mprtant features, lke all-weather functn, beng easy t perate, hgh ecnmc effcency f navgatn pstnng, and tmng systems. Its advantages nclude all-weather functn free frm any nterference, glbal cverage up t 98%, three-dmensnal fxed cnstant speed precsn, tme-savng, hgh effcency, wdely used, versatle, and mble pstnng [10]. In ths paper, the GPS recever used (Fgure 3)treceve GPS satellte sgnals s manufactured by ICP DAS Cmpany, mdel GT-321R. Usng RS-232 seral prt, prtcl settng as 4800 bps baud rate and 8-N-1 frmat. The fur messages t retreve manly ncludng lngtude, lattude, speed and tme, s NMEA nfrmatn utput frmat (GPRMC) message was chsen as the desgned USV pstn nfrmatn n the marne envrnment and salng speed Drectn Sensr. Drectn sensr s called the electrnc cmpass (e-cmpass), the mst sutable range fr the Earth s magnetc feld detectn. DC statc magnetc feld can be detected; t can detect the magnetc feld strength and drectn. Earth s magnetc feld strength f 0.5 t 0.6 gauss can be smplfed as shwn n Fgure 4 f bplar magnetc feld, whch s equvalent alng centre f the earth.

3 Advances n Artfcal Intellgence 3 Tp vew: X Fgure 4: Earth s magnetc feld. Z Y Fgure 6: Installatn dagram f drectn sensr. Fgure 5: Drectn sensr [8]. Electrnc cmpass pntng alng the lcal magnetc feld determnes the drectn, the drectn f the lcal magnetc nrth usually. Because the magnetc nrth and true nrth are nt the same, s the magnetc nrth and true nrth are nt usually tgether. The lcal magnetc varatn s called magnetc declnatn (declnatn angle). It s a smple descrptn f the magnetc nrth and gegraphc nrth dfference between the angles, expressed as easterly r westerly drectn. Electrnc cmpass whch suffered all knds f nterference can be bradly dvded nt tw categres. (1) Hard Irn Interference. Fxed-ntensty magnetc nterferences, such as sensr surrundng the rgnal electrnc parts, such as speakers, mcrphnes, batteres, panels, and metal sheld, wll release a fxed magnetc frce t nfluence the electrnc cmpass f readng. Calbratn must be dne t zer. (2) Sft Irn Interference. It wll change the ntensty and drectn r can dstrt the magnetc feld lnes f nterferng substances, such as battery electrcty cnsumptn changes n the user s envrnment; surrundng the rgnal electrnc parts f nterference depends n precsn quas-level specfcatns t determne the need fr crrectn [11]. The UM6 ultramnature rentatn sensr measurng rentatn n all three dmensns at 500 Hz usng a cmbnatn f rate gyrs, accelermeters, and magnetc sensrs was appled t mntr salng drectn as shwn n Fgure 5. The drectn sensr was munted n desgned USV and was alng wth the drectn f the bat as shwn n Fgure 6. Hence, the Earth s magnetc feld and USV salng drectn wll be dentcal Cntrller Cre System. Thnkng f sea envrnment, we must pay mre attentns t chse a mre relable cmputer as the unmanned vehcle cntrller, and the selected cntrller s capablty must meet the basc requrements fr the prcessng speed and vbratns frm utsde nterference. Andtmustwthstandhghertemperatureranges.Duetthe fact that the cmplexty f autplt peratn s very hgh, and the wave n sea level s greater, the calculatn ablty and prcessng speed f central cntrller must be cnsdered. Natnal nstruments CmpactRIO (crio) s qute applcable t the prevus requrements. The selected crio s a prgrammable autmatn cntrller (prgrammable autmatn cntrller, PAC), a lw-cst, recnfgurable cntrl, and acqustn system fr the need fr effcent perfrmance and relablty applcatns s desgned. The system s dne thrugh a small, rugged, ndustral-grade ht-swappable nput and utput (nput/utput) mdule, nt nly crio t have ndustral-grade archtecture, but als can be placed n the factry r under nclement envrnment ensure that the system the relablty [12]. In ths system, due t the need f external nput fr recevng cntrl cmmands, (1) RS-232 seral transmssn (GPS recevers, drectn sensrs, and left turn servmtr), (2) DO dgtal sgnal utput (status dsplay), (3) AO analg utput (pwer utput f the mtr fr cntrl), selected nterface card must cmply wth the prevus specfcatns. Three mdule adapter cards n ths ptnal mdule fr natnal Instruments CmpactRIO are, respectvely, NI- 9870, NI-9403, and NI Each mdule card nt NI-9074 chasss has been shwn n Fgure Steerng Mechansm. In ths paper, a small bat whch can affrd 5 6 passengers and has utbard rudder wth DC mtr as pwer s adpted as USV man bdy. In rder t acheve unmanned autmatc navgatn functn, a mechancal devce must be drven n the steerng wheel t take cntrl f salng drectn. Therefre, n ths paper Mtsubsh

4 4 Advances n Artfcal Intellgence User s applcatn layer Ntebk AP Lnkng layer GPS Servmtr NI-9870 USV system layer Fgure 7: crio hardware platfrm. crio-9074 Drectn sensr Status ndcatrs NI-9403 NI-9263 Brushless mtr Fgure 9: Archtecture f the unmanned autmatc system. Fgure 8: MR-J2S-10A servmtr [9]. MR-J2S-10A servmtr s used t drve, as shwn n Fgure 8. A servmtr wth hgh precsn pstn cntrl and baud rate f 9600 bps va an RS-232 seral prt by way f trque and speed cntrl s adpted n ths desgn. 3. Archtecture and Research Methd There are three layers n the system archtecture, as shwn n Fgure 9. The frst layer s the user s applcatn nterface f the cnsle. It s fr perceptn f vyage nfrmatn r delvery f mssn cmmands. The secnd layer s the lnkng layer fr ntercnnectn. Ths s fr data transmssn between the tw ends, va wreless netwrk statns. The thrd layer s the system layer the cre f ths unmanned autnavgatn system. It cntrls the steerng and dynamc f the whle bdy thrugh precse calculatn. Ch-Chn harbr s the ste where testng f autnavgatn n ths experment was cnducted. The test vessel s a duble-hull bat, desgned by the department f naval archtecture and cean engneerng, and s equpped wth a brushless utbard mtr as the engne f the vessel. The prmary sensng nstruments used n the test are GPS and rentatn sensr, and crio-9074 s used fr calculatn and ustfcatn f the autmatc navgatn system t cntrl the steerng and pwer-utput f the utbard mtr. GPS f the autnavgatn system s nstalled n tp f the vessel, as Fgure 10: Illustratn f layut f equpment. shwn n Fgure 10. The rentatn sensrs are nstalled n frnt f the cckpt, n lne wth the rentatn f the vessel bdy. 4. Desgn f Cntrl Algrthm Based n Imprved ENN wth TSK Fuzzy In rder t desgn the autmatc navgatn cntrl system wthut sea wave nferences, a lt f effrts are used t desgn the fuzzy and neural netwrk cntrl. Hence, fuzzy cntrl prcess prceeds t the amunt f cntrl requrement [13, 14]. Intellgent cntrl f nnlnear systems capable f handlng and uncertanty, especally n the cmparsn f PID and fuzzy cntrl, usng the fastest desgn f neural netwrk cntrl, even n the utput cntrl can mprve accuracy [15], s n ths fuzzy neural netwrk cntrl thery wll be used as an autmatc navgatn system cntrl Imprved Elman Neural Netwrk (ENN) Cntrller. The archtecture f the prpsed mprved ENN ncludng

5 Advances n Artfcal Intellgence 5 Supervsed learnng law + USV Ref. angle Rudder angle y (4) where x (1), x (3) r are nput and x (2) (k) s utput f the hdden layer. x (3) r (k) s als the utput f the cntext layer; W and W r are the cnnectng weghts f nput neurns t hdden neurns and cntext neurns t hdden neurns, respectvely. Layer 3: Cntext Layer. Inthecntextlayer,thendenput and utput are represented as Output layer W Σ x (3) r (k) =αx (3) r (k 1) +x (2) (k 1), (3) where 0 α<1s the self-cnnectng feedback gan. Z 1 x r (3) Z 1 Z 1 W r Z 1 Z 1 Z 1 α α α Cntext layer r S S Hdden layer S Input layer e (1) Fgure 11: Archtecture f the mprved ENN. the nput layer, the hdden layer, the cntext layer, and the utput layer wth tw nput ndes, nne hdden ndes, and ne utput nde s shwn n Fgure 11,wherethecntrllaw s defned as rudder angle, and the tw ENN nputs are e (1) 1 and e (1) 2 wth e (1) 1 = e(k) and e (1) 2 =ce(k)=e(k) e(k 1),the change f errr. Fr the kth samplng nstant, the errr can be expressed as angular devatn e(k) = θ r (k) θ r(k). The prpsed ENN [4, 5] takes the feedback nt accunt, and better learnng effcency can be btaned. Mrever, t make the neurns senstve t the hstry f nput data, self-cnnectns f the cntext ndes and utput feedback nde are added. S the prpsed ENN has the ablty t deal wth nnlnear prblems and can effectvely mprve the cnvergence precsn and reduce the learnng tme. The sgnal prpagatn and the basc functn n each layer are ntrduced belw. Layer1:InputLayer.In the nput layer, the nde s defned by net (1) =e (1) (k), x (1) (k) =f (1) (net (1) (k)) =net (1), = 1,2, where k represents the kth teratn; e (1) (k) and x (1) (k) are the nput and the utput f the layer. Layer 2: Hdden Layer. In the hdden layer, the nde s defned by net (2) = x (2) (k) = W x (1) (k) + W r x (3) r (k), r 1 =1,2,...,9, 1+exp ( net (2) ), x (2) W x (1) (1) (2) Layer4:OutputLayer.In the utput layer, the nde nput and utput are represented as y (4) (k) =f (4) (net (4) (k)) =net (4) (k), net (4) (k) = W x (2) (k), where W s the cnnectng weght f hdden neurns t utput neurns and y (4) (k) stheutputfthemprvedenn andalsthecntrllawftheprpsedcntrller. (4) 4.2. Onlne Supervsed Learnng and Tranng Prcess. Once the mprved ENN has been ntalzed, supervsed learnng s used t tran ths system based n gradent descent thery. The dervatn s the same as that f the backprpagatn (BP) algrthm. It s emplyed t adust the parameters f the ENN by usng the tranng patterns. By recursve applcatn f the chan rule, the errr term fr each layer s frst calculated. The adaptatn f weghts t the crrespndng layer s then gven. The purpse f supervsed learnng s t mnmze the energy functne expressed as [16] E= 1 2 (θ r θ r) 2 = 1 2 e2 L, (5) where θ r and θ r represent the angle utput reference and actualangleutputftheusv,respectvely,ande L dentes the trackng errr. The learnng algrthm s descrbed belw. Layer4:UpdateWeghtW. The errr term t be prpagated s gven by δ = E net (4) =[ E net (4) Then the weght w s adusted by the amunt ΔW = E W =[ E and updated by where η 1 s the learnng rate. net (4) ]. (6) ]( net(4) )=δ W x (2) (7) W (k+1) =W (k) +η 1 ΔW, (8)

6 6 Advances n Artfcal Intellgence Layer3:UpdateWeghtW r.byusngthechanrule,the update law f W r s ΔW r = E W r =[ E =δ W x (2) net (4) [1 x (2) ]x(3) r. ]( net(4) x (2) The cnnectng weght W r s updated accrdng t where η 2 s the learnng rate. x (2) W r ) (9) W r (k+1) =W r (k) +η 2 ΔW r, (10) Layer 2: Update Weght W.Byusngthechanrule,the update law f W s ΔW = E W =[ E =δ W x (2) net (4) [1 x (2) ]x(1). ]( net(4) x (2) The cnnectng weght W s updated accrdng t where η 3 s the learnng rate. x (2) W ) (11) W (k+1) =W (k) +η 3 ΔW, (12) 4.3. Takag-Sugen-Kang (TSK) Fuzzy Cntrller. Typcally, a TSK fuzzy mdel cnssts f IF-THEN rules that have the fllwng frm: R :fx 1 s A 1, x 2 s A 2,...,andx n s A n,then h =f (x 1,x 2,...,x n ;a )=a +a 1 x 1 + a n x n. (13) Fr = 1,2,...,C,whereC s the number f rules, A s the fuzzy set f the th rule fr x wth the adustable parameter set θ,anda =(a 0,a 1,...,a n ) s the parameter set n the cnsequentpart.thepredctedutputfthefuzzymdels nferred as [16] y = C =1 h w C =1 w, (14) where h s the utput f the th rule; w = mn =,+1,...,n A (θ ; x ) s the th rule s frng strength, whch s btaned as the mnmum f the fuzzy membershp degrees f all fuzzy varables. There are many chces fr the types f membershp functns, such as trangular, trapezdal, r Gaussan. In ths paper, a Gaussan membershp functn s emplyed fr tw reasns. Frstly, a fuzzy system wth Gaussan membershp functn has been shwn t apprxmate any nnlnear functns n a cmpact set. Secndly, a multdmensnal Gaussan membershp functn generated durng the learnngprcesscanbeeaslydecmpsednttheprductf1d Gaussan membershp functns. Chsng Gaussan membershp functn, n (16), the parameters f the premse parts (.e., θ )ncludem and σ,whcharethecenter(rmean) and the wdth (r varance) f the Gaussan membershp functn f the th rule at th dmensn, respectvely. Bth the premse parts (.e., θ ) and the cnsequent parts (.e., a ) n a TSK fuzzy mdel are requred t be dentfed [17, 18]. The cnsdered prblem s t btan crrect dstrbutn f fuzzy rules and ts crrespndng plynmal frm a set f bservatns. The nput-utput pars are {(x 1, y 1 ), (x 2,y 2 ),...,(x N,y N )},wherex k =k/ns nrmalzatn f kth subcarrer channel number; y k = real( H p (k)) s the real part (r magnary part) f the crrespndng channel transfer functn. We assume that thse bservatns are btaned frm an unknwn functn y k =f(x k ).Wewant t cnstruct a TSK mdel that can accurately represent f n terms f nput-utput relatnshp. In rder t smplfy the algrthm and lse the cmpute burden, we fx the number f rules as C=N/2and the parameters f each rule as m = 2 1 N, (15) σ = 2 N, (16) where m and σ are the center and wdth f the membershp functn, respectvely. The fre strength f each nput represents the degree x k belngng t the crrespndng rule. Snce the nput s ne dmensn, the fre strength can be calculated by F =e [(x k m ) 2 /σ 2 ]. (17) Snce the nrmalzed frng strength s emplyed, the w n (14) can be defned as w = F. (18) F Furthermre, the parameters f each rule are fxed; the nly adustable parameter f TSK mdel s a n (13). The parameter s updated by the fllwng rule: a (t+1) =a (t) +η[ y k y k ] x (k) w, (19) where x(k) s nput vectr [1, x k ], η s the learnng rate, and y k s the current utput f fuzzy mdel calculated usng (14). Fnally, the prcedure f the used TSK learnng algrthm s descrbed as fllws. Step 1. Defne the fuzzy rule n (15)and(16); the ntal value f a s set t be [1, 1]. Step 2. The frst snapsht f all-plt subcarrers s used t tran the n a (13) t(19). When the errr s small enugh, theng tstep 3. Step 3. Estmate the channel transfer functn usng (13) t (18). When the nput s at plt symbl channel, a s updated ttracethevaratnfchannel.ablckdagramfthetsk fuzzy cntrller s presented n Fgure 12. Thepwercntrl wth a prcessng flwchart s shwn n Fgure 13.

7 Advances n Artfcal Intellgence 7 Table 1: Test f lnear acceleratn. Pwer (%) Task Average speed (NM) Start Angular devatn, current speed, and prevus pwer utput sgnal TSK fuzzy f pwer utput Fuzzy rule Fuzzfcatn Fuzzy reasnng Defuzzfcatn Reach fnal pnt N Angular devatn Yes Current speed Prevus pwer utput Pwer utput End Fgure 13: Flwchart f TSK fuzzy cntrl. Fgure 12: Blck dagram f TSK fuzzy cntrl f pwer utput Test f the Unmanned Autmatc System Frbtanngthebascdynamcdatafthebat,tsnecessary t test the bat n drvng alng a straght lne and n turnng drectns. Befre the desgn f an unmanned autnavgatn, t s requred t fgure ut the dynamc pwer fr cntrllng the speed f the bat as well as the steerng characterstcs and adequate turnng speeds. After cllectn f requred data, desgn and prgrammng f mprved ENN wth TSK fuzzy cntrl can then be preceded. The crio and cmputer are nstalled n the bat t recrd bat s relevant data. The speed data f the bat s btaned va GPS, andcmputersusedtcntrlthepwerutput Straght Mvement. Data abut the dynamc f lnear acceleratn s cllected durng the test. As llustrated n Fgure 14, tsrevealedthatthebat smanspeedranges manly NM/h. Due t the desgn f duble-hulled frame and a weght f 1.3 tns, the maxmum speed s n mre than 3 NM/h. Mrever, even at lw startup speed, ntated wth 30% maneuver pwer, t tk a bt lnger tme t acheve wrkng speed. Frm Table 1,tsclearthatbycntrllngthepwern the range f 50 85%, vessel per hur salng s arund NM/h, whch s mre stable n mvement Turnng Radus. When testng turnng mvement, the man methd s t test speeds n segmentatn t btan the radusfrturnngthebatfr360. Frm ur result, t s bvusthatat40%fpwerutput,theturnngfthebat s almst spnnng n the spt. It reveals the better stablty wth the desgn f the duble-hulled frame. Nnetheless, the Speed (knt) Speed Pwer Tme (s) Fgure 14: Test f lnear acceleratn. pwer utput shuld be greater than 40% relatvely effectve perfrmance f there s the need fr mvng frward whle turnng. 6. Expermental Results The autmatc navgatn desgn s shwn n the dagram f Fgure 12; the mdule f mprved ENN cntrl s needed fr mdfcatn f rentatn f the bat s devatng frm ts navgated drectn. In addtn, fr the cntrl f pwer utput, data abut angular devatn, current speed, and prevus pwer are requred fr mantanng the bat at wrkng speed thrugh TSK fuzzy cntrl. The USV autmatc navgatn test begns wth nputs f destned navgatng pnts, as llustrated n Fgure 15, and then the nfrmatn s transmtted t the crio-9074 system Pwer (%)

8 8 Advances n Artfcal Intellgence Table 2: Perfrmance f prpsed cntrl methd Methd Imprved ENN wth TSK fuzzy methd Trackng dstance errr (m) Angular respnse (s) X/Y pstn respnse (s) ± Speed (knt) Pwer (%) Speed Pwer Tme (s) Fgure 17: Navgatn data f pwer and speed Fgure 15: Cmmand panel f PC sever. Angle ( ) Target declnatn Shp declnatn Tme (s) Fgure 18: Data f target declnatn and bat declnatn. t keep n the navgated drectn. The perfrmance f prpsedcntrlmethdssummarzedntable 2. Fgure 16: Navgatn rute. n the bat. When the server presses the autmatc navgatn buttn, the bat begns a vyage alng the destned navgatng pnts. Data transmtted back frm crio-9074 ncludes pwer utputs, speeds, and tracks f the bat fr realzatn f the navgatn cndtns. It s bvusly llustrated n Fgure 16 that USV fllws the destned rute successfully n navgatn. Fgure 17 shws the navgatn data f ths test trp. It reveals clearly that the bat successfully fllwed the desgn f TSK fuzzy cntrl, t mantan the speed f mvng straght frward at arund 2.2 NM/h and speeds f turnng at arund NM/h. When turnng, t s demnstrated n Fgure 18 that the bat declnatn has fllwed the target declnatn. It s als clearly llustrated that when the bat was rdered t turn at 90 secnds, t dd adust the rentatn effectvely 7. Cnclusn Ths study uses a platfrm develped by the department f naval archtecture fr the autnavgatn system, usng GPS, rentatn sensrs, and crio-9074, as well as cmbnng LabVew and ntellgent cntrl algrthm, t reduce the cst f sensrs and tme f system develpment effectvely. Fr the autnavgatn system, there are tw sets f ntellgent cntrl algrthm, desgned fr pwer utput and steerng f ths electrc duble-hulled bat, t cntrl the bat stably t travel alng the navgatn rute. As the wave heghts n Ch-Chn harbr vary dramatcally, the test results shw that the duble-hulled bat has pretty gd stablty and keeps steerng cntrl. The task f autnavgatn culd be easly and ncely perfrmed. Cnflct f Interests The authrs declare that there s n cnflct f nterests regardng the publcatn f ths paper.

9 Advances n Artfcal Intellgence 9 References [1] B. Vlker, Unmanned Surface Vehcles A Survey, Skbsteknsk Selskab, Cpenhagen, Denmark, [2]C.S.Huang,An ambulate wreless cntrl rbtc arm n the applcatn f ant-explsn [M.S. thess], I-Shu Unversty, Tawan, [3] H. F. Ku, Desgn and mplementatn f a remtely cntrlled rbt-car wth real-tme mage dentfcatn technque fr bect trackng [M.S. thess], Natnal Cheng Kung Unversty, [4] X. L, G. Chen, Z. Chen, and Z. Yuan, Chatfyng lnear Elman netwrks, IEEE Transactns n Neural Netwrks,vl.13,n.5, pp , [5] F.-J. Ln and Y.-C. Hung, FPGA-based elman neural netwrk cntrl system fr lnear ultrasnc mtr, IEEE Transactns n Ultrasncs, Ferrelectrcs, and Frequency Cntrl,vl.56,n. 1, pp , [6] M. Sugen and G. T. Kang, Structure dentfcatn f fuzzy mdel, Fuzzy Sets and Systems, vl. 28, n. 1, pp , [7] Glbal Pstnng System, [8] CHR-UM6 Drectn Sensrs, [9] MR-J2S-A, [10] S. H. Tsa, Research and develpment f unmanned aeral vehcle cntrl sftware [M.S. thess], Natnal Defense Unversty, [11] Acceleratn Sensr and Electrnc Cmpass Prncple Intrduced, supprts pdf. [12] NI CmpactRIO, [13] S. C. Lu, Autmatc navgatn f a wheeled mble rbt usng partcle swarm ptmzatn and fuzzy cntrl [M.S. thess], Natnal Central Unversty, [14] Y. H. Ln, Integrated flght path plannng system and flght cntrl system fr navgatn and gudance f unmanned helcpter [M.S. thess], Natnal Cheng Kung Unversty, [15] C.M.Hng,T.C.Ou,andK.H.Lu, Develpmentfntellgent MPPT cntrl fr a grd-cnnected hybrd pwer generatn system, Energy, vl. 50, pp , [16] C. T. Ln and C. S. G. Lee, Neural Fuzzy Systems, Prentce-Hall, [17] S. X. Yang, H. L, M. Q.-H. Meng, and P. X. Lu, An embedded fuzzy cntrller fr a behavr-based mble rbt wth guaranteed perfrmance, IEEE Transactns n Fuzzy Systems,vl. 12,n.4,pp ,2004. [18] T.-M. Wang, P.-C. Ln, H.-L. Chan, J.-C. La, T.-W. Sun, and T.-Y. Wu, Energy savng f ar cndtn usng fuzzy cntrl system ver Zgbee temperature sensr, n Prceedngs f the 24thIEEEInternatnalCnferencenAdvancedInfrmatn Netwrkng and Applcatns Wrkshps (WAINA 10),pp , Aprl 2010.

10 Jurnal f Advances n Industral Engneerng Multmeda The Scentfc Wrld Jurnal Appled Cmputatnal Intellgence and Sft Cmputng Internatnal Jurnal f Dstrbuted Sensr Netwrks Advances n Fuzzy Systems Mdellng & Smulatn n Engneerng Submt yur manuscrpts at Jurnal f Cmputer Netwrks and Cmmuncatns Advances n Artfcal Intellgence Internatnal Jurnal f Bmedcal Imagng Advances n Artfcal Neural Systems Internatnal Jurnal f Cmputer Engneerng Cmputer Games Technlgy Advances n Advances n Sftware Engneerng Internatnal Jurnal f Recnfgurable Cmputng Rbtcs Cmputatnal Intellgence and Neurscence Advances n Human-Cmputer Interactn Jurnal f Jurnal f Electrcal and Cmputer Engneerng