DTIC ADA436011: Shallow Water Stationkeeping of an Autonomous Underwater Vehicle: The Experimental Results of a Disturbance Compensation Controller PDF
Preview DTIC ADA436011: Shallow Water Stationkeeping of an Autonomous Underwater Vehicle: The Experimental Results of a Disturbance Compensation Controller
Shallow Water Stationkeeping of an Autonomous Underwater Vehicle: The Experimental Results of a Disturbance Compensation Controller lede i.RS.J hcrae s VrerUoReAftneC loo hectSaudargt sloaPvaN 3 4 ,9Ay3Ce9retnoM lim.yvan.spn.em@ledeirsj ,ylts a,tLyil ltaa thsnti eenmhwitorh esCpCxDe s ielbatdsna ABSTRACT swolla eht SPN xineohP VUA ot dloh noitisop ni ehtegrus .y ayBeretn onsMiev anwae codotetcejb uesli h,wnoitcerid The continual development of computer technology has enabled the expansion of intelligent control into the field of A. DCC Overview underwater robots, where potential uses include oceanographic research, environmental monitoring and military mine ehT ngised fo eht ecnabrutsid noitasnepmoc countermeasures. With the naval focus shifting to operations in rellortnoc nac eb dekool ta sa na noitazimi tmpeolborpecnis the littorals, and the need to lower cost of operations, tetherless ereht era gnitepmoc .slaog ,tsriF ecnis eht ngised autonomous vehicles are now being proposed for use in very tnemeriuqer si ot eziminim noitisop rorre ni eht ecneserpfo shallow water minefield reconnaissance. These areas are ,secnabrutsid a hgih niag lortnoc si .elbarised gnisUhgih dominated by a highly energetic environment arising from niag ,lortnoc eht metsys semoceb evitisnes ottnemerusaem waves and currents. Motion control in such an environment becomes a difficult task and is the subject of this work. esio n,y tdynnbia eagrtnerihestcunau ceht niag ot ebdecuder The main objective of this paper is to show that .ytilib antisatn ioatm intervention tasks performed by intelligent underwater robots nA rotamitse si dedeen ot edivorp eht elbarusaemnu are improved by their ability to gather, learn and use setats ot eht ,rellortnoc dna ot retlif eht rosnes esionybereht information about their working environment. Using a new tnseimer i. ue,eqc ehenrtraemHro fsrmeep tgesnhyitsvorpmi generalized approach to the modeling of underwater vehicles, ot yletarucca kcart eht ,langis niaga gniriuqer a hgihretlif which directly includes disturbance effects, a new Disturbance ,niag elihw gnihtooms eht ,esion a( wol .)niag sA htiweht Compensation Controller (DCC) is proposed. The DCC, -edart ,rellortnoc .ed aetmbs usmffo employing onboard vehicle sensors, allows the robot to learn ehT revo lla laog si ot poleved a denibmoc and estimate the seaway dynamics. This self-derived knowledge is embedded in a non-linear sliding mode control law which rotamitse/rellortnoc ,hcihw nehw ,detnemelpmi lliwelbane allows significantly improved motion stabilization. The eht elcihev ot niatniam noitisop elihw gnisu ysionrosnes performance of the DCC has been experimentally verified in .noitamrofni ehT tuptuo fo siht ,metsys rof,noitatnemelpmi Monterey Harbor using the NPS Phoenix AUV. si edge adtatnalahomtv msoic tn emsor feht sCsCeDcoortp eht emit-laer noitucexe ,retupmoc tuohtiw evissecxe sgalot I. NOITCUDORTNI erusne .ytilibats A lacitamehtam noitpircsed ot ehtevoba sihT repap lliw ssucsid eht tnempoleved dna melborp si nevi g,woleb htiw a kco lmbargaid fo eht CCDni tnemyolpme fo a emit-laer ecnabrutsid noitasnepmoc .e1rug i nFdiedivorp rellortnoc )CCD( hcihw lliw wolla na VUA otyllacimanyd State: xT =[X, u , F]; d=u fnlo e iesntcti ini .esefshosoetpevra pw er helnTpliaigpweb System: x&= f( x, n,rd;) yT =[fx, u , u ,]=Cx+Dd. (1) ht iww eniav rfeov o edh et,wCo ClyDl bonfoi s asfuocsinda r g Disturbance: x& =Ax +nn u =Cx suonorhcnysa dednetxE namlaK retliF rof etats dna f f f f .enconiatbarmuittssi ed s irh aTreontialmniotns esi lacitircot Control law: n=smc( xˆ ,uˆ ,x ) [ ] f com eht CCD ecnamrofrep ecnis eht gnidilS edoMrellortnoC Estimator: xˆ , uˆ =EKF( f( x, n, d), A, y, n) f )CMS( seriuqer lluf etats ,kcabdeef dna ton lla setatsera .elbarusaem nI ,noitidda eht FKE sedivorp ehtrellortnoc B. State And Disturbance Estimation htiw a dehtooms etamitse fo eht derusaemnu diulfelcitrap yticolev hcihw si desu ot etasnepmoc rof eht evawdecudni d nsaeta testamit s oeetlbalia vsadoht eymn aem rearehT .ecnabrutsid eehdtul c en.siy ea wfh dAeoteofctitc asnreipcnabrutsid ,txeN hguorht eht ngised dna noitatnemelpmi fona rae n ri ]olr2fe[t lniaFm l ea dhK ]ntr1ae[vre srbeOgrebneuL suonorhcnysa ,rotalumis hcihw yllacitsilaer sledom eht e "h,t] 3r[evres beOd ogMnidi le Shd tn,asmetsys "inamajaR elcihev ,scimanyd eht srosnes gnidulcni esion dna ehtrosnes r o]f 5r[etl inFaml adKednet xeE hdt n]a 4r[evresbO ,sessecorp eht CCD si denut dna eht elbaveiehccnaamrofrep sno cdn asor phto bsa hdohte mhc a .Esmetsy sraenilnon .detarts nsoimed dednet xn,Eakr oswi h.r tnooFitacilp pe a hngtonidneped elcih eevtaruc cyalevita l eeacrn inseso hs carwetl inFamlaK Report Documentation Page Form Approved OMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE 3. DATES COVERED 2005 2. REPORT TYPE - 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Shallow Water Stationkeeping of an Autonomous Underwater Vehicle: 5b. GRANT NUMBER The Experimental Results of a Disturbance Compensation Controller 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION Naval Postgraduate School,Center for AUV REPORT NUMBER Research,Monterey,CA,93943-5000 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR’S ACRONYM(S) 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 12. DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release; distribution unlimited 13. SUPPLEMENTARY NOTES The original document contains color images. 14. ABSTRACT The continual development of computer technology has enabled the expansion of intelligent control into the field of underwater robots, where potential uses include oceanographic research, environmental monitoring and military mine countermeasures. With the naval focus shifting to operations in the littorals, and the need to lower cost of operations, tetherless autonomous vehicles are now being proposed for use in very shallow water minefield reconnaissance. These areas are dominated by a highly energetic environment arising from waves and currents. Motion control in such an environment becomes a difficult task and is the subject of this work. The main objective of this paper is to show that intervention tasks performed by intelligent underwater robots are improved by their ability to gather, learn and use information about their working environment. Using a new generalized approach to the modeling of underwater vehicles, which directly includes disturbance effects, a new Disturbance Compensation Controller (DCC) is proposed. The DCC, employing onboard vehicle sensors, allows the robot to learn and estimate the seaway dynamics. This self-derived knowledge is embedded in a non-linear sliding mode control law which allows significantly improved motion stabilization. The performance of the DCC has been experimentally verified in Monterey Harbor using the NPS Phoenix AUV. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF 18. NUMBER 19a. NAME OF ABSTRACT OF PAGES RESPONSIBLE PERSON a. REPORT b. ABSTRACT c. THIS PAGE 8 unclassified unclassified unclassified nciitsahco t sseicnabruts ied hetcn ids n,aelbalia v saliedom x& =u +u nature. r f dnuorG u& =a u u +F yticoleV r r r y r + - evitaleR - 1 g b yticoleV yticoleV diulF F& = F + u n + nn noitisoP t t r t NavF ilter Estimate State Filter ncom x&w,1 = xw,2 =uf , (2) x& =u& = x w,2 f w,3 x& = a x +a u +a x +a x w,3 1 w,1 2 f 3 w,3 4 w,4 x& =n Controller Estimates State sr ontooimsluporp Voltage to yw=,4 [ x, u , u ] T r g vcom OS9 erehw eht RA stneiciffeoc era dnuof gnisu eht naenilno n com .le deo vmeavwitpada Figure1 .B lockD iagramo fD isturbanceC ompensationC ontroller( DCC) namlaK gniretlif si eht ssecorp fo ylevisrucer A. Kalman Filter Algorithm gnitadpu na etamitse fo smetsys setats desab nopu stnemerusaem detpurroc yb .esion ehT metsys etats sia gnisU dradnats ngised seuqinhcet ,]2[ eht retlifsaw , m sefct oisamyas neeyhbdtirc st easdhetlba i nrfoaoivtcelloc depoleved dna detnemelpmi gnisu eht gniwollof.mhtirogla dna ni siht esac yeht era ,noitisop evitaler yticolev dna ,tsriF eht metsys ledom xirtam A , metsys esion xirtam Q, relleporp ,tsurht fo hcihw ylno evitaler yticolev si xtinretmaemrusaem C t,neme reussiaoe nmxirtam R , dnaeht .elbarusaem rorre ecnairavoc xirtam P era dezilaitini ot etairporppa metsyS etats era detadpu htiw egdelwonk fometsys .seulav ee chx nTiraroitrrar amesvioc a nac etbhguo hftosa scimanyd elcihev( ,)ledom tnemerusaem scimanyd a level fo ytniatrecnu ni eht etats .rotcev nehT ehtetats tnemerusaem( ,)ledom metsys esion gniledom()ytniatrecnu ,rotcev rorre ecnairavoc dna tnemerusaem rotcev era dna tnemerusaem esion tnemerusaem( .)srorre ehTmetsys .ledo meh tgnis upet semi ten odetagaporp s cgl i enef me lid otahscboecn tiiimh eythrtnfdoecirnvseepd nehW eht wen tnemerusaem si ,deviecer dna dna lliw niatnoc a niatrec tnuoma fo ,ytniatrecnudellac neew teecbneref f eidnhdeot sdaebtaluc lrsaoic rnroeitavonni met s.yession erehT si os l daye ttenamiioachstotrsieswcanu eht derusaem seulav dna eht detamitse .seulav gnisUeht hcae tnemerusa e.mnekat sihT ytniatrecnu nac ebdesopmoc detagaporp rorre ,ecnairavoc tnemerusaem esion xirtamdna fo hm toe odtebnisahi rwodnna a .saib stnemerusaeMhcihw t n,exmierrtuasma eam niag d esniim rreotfed eht etartostcev ton n,a dcyeel bnthiccaeutrsbio ds a, ydtiiu cleodfrleaetvaler gnd in.ta ea stgesa a cedfpsncpooiaourhirp Trp radovnroarce ot stnemerusaem hcihw era yltcerid ,elbaniatbo hcus sa gnitadpu si detaeper hguorht tuo eht htgnel fo ehtelcihev evitaler yticolev dna dnuorg ,yticolev ni ehttnemerusaem .noissim sihT evisrucer ,mhtirogla ni etercsid mrof sinevig .ledom ylevisruceR gnitadpu snaem eht namlaK retlifseod ,yb ton deen ot peek drocer fo lla tsap ,stnemerusaem ylnoeht ¶ f( x , n) .sen otnece rtsom FF = aug k/ k- 1 ¶ x II. TNEMPOLE VREEDT L DILNFEADOM aug xˆ k- 1/k-1 xˆ =FF xˆ Using a eerht etats egrus ledom ]6[ dna a ruofetats k/ k- 1 k/ k- 1 k- 1 / k- 1 RA ledom rof eht evaw scimanyd ,]7[ na tnemgua etatsdna P =FF P FF T +Q , (3) ecnabrutsid ledom saw ,demrof dna desu sa eht sisab fona k/ k- 1 k/ k- 1 k- 1 / k- 1 k/k- 1 .FKE sihT ledom swolla eht ecnabrutsid ot eb detaert sana Gk = Pk/ k- 1 hkT[hkPk/ k- 1 hkT +R]- 1 lanoitidda ,etats erehw eht elcihev setats dnaecnabrutsid xˆ = xˆ +G [ y - h xˆ ] setamitse era retlif .stuptuo ehT detnemgua elcihevdna k/ k k/ k- 1 k k k k/ k- 1 ,nyeb vlsieig deocmnabrutsid P =[ I - G h ] P k/ k k k k/ k- 1 erehw FF stneserper eht metsys ,scimanyd dna h=C ecniseht stnemerusaem era raenil ni eht .etats ehT suounitnoc , snaev ieg rnagis erdalucitr aspi hrt osfecirt admeziraenil Ø 0 1 0 0 1 0 0 ø 14 Œ œ Œ 0 2a uˆ rsign(uˆ r ) 1 0 0 0 0 œ 12 b - 1 Œ 0 n 1 0 0 0 œ Œ t com t œ 10 AA =Œ 0 0 0 0 1 0 0 œ . (4) P Œ œ 8 0 0 0 0 0 1 0 Œ œ Œ 0 0 0 - 1 - 4 - 6 - 4 œ 6 Œ œ º 0 0 0 0 0 0 0 ß 4 Ø 1 0 0 0 0 0 0 ø Œ œ 2 C = 0 1 0 0 0 0 0 Œ œ Œº 0 0 0 0 1 0 0 œß 0 0 002 004 006 008 0001 Digital Samples FiguDr2Ce E.C r roCro variancEev olution 1. Asynchronous Data Processing IV. GNIT SREETTAW -LNAIITINI denehintiat na o tec,ahndtoissuc sgindidec eenrhIpt stnemerusaem saw demussa ot eb deviecer ta eht emasemit gnisU trohs ,snoissim eht CCD saw detsujda ot htiw lauqe slavretni hguorht tuo eht .noissim nI ,ytilaerlla . eeecvlnebaiamhtrcpoaefcr ceap esehT snur erdeewmrofrep , ee drhse o ttveft n ire ate,eeareomehcemnetteharartsursaem no hcraM ,52 ,9991 ni yeretnoM .robraH fO ,nrecnocsaw FKE ngised tsum wolla rof siht suonorhc ngynsialpmas.etar eht tnuoma fo esion taht saw tnediser no eht VDArosnes . nI eht xineohP ,VUA eht elcihev lortnoc pool snur ta 8,zH sihT esion saw raf dnoyeb eht level hcihw eht rodnev elihw eht IDR LVD snur ta 2 ,zH dna eht keTno SVDA ta6 ,s en.hodt ie stsm ta iogllnet rnuughrefismitehseisvtUrsedda .zH e e.]sSs 7lr r [ioeeon asrsfotnoe eem hedsnthaiTtaamd CCD saw detnemelpmi ni eht xineohP .VUA serugiF5-3 noitisiuq csasecorp selpmas eht rosnes sessecorp ta ehtemas yalpsid laitini .stluser sA ,nees eht retlif skcart ehtslangis ycneuqerf sa eht lortnoc ,pool ,revewoh fi eht rosnes sahton .esio neh tgnidulcn i,lle wylemertxe tey ,detadpu eht antoaidt isssieucq ocsradprocer eht eulavfo eht suoiverp emit .pets ehT retlif swolla rof ehtgniyrav 1.1 X tnemerusaem setar yb gnisu a cimanyd gnihctiws fo eht Xhat tnemerusaem ,xirtam C . ehT tnemerusaem xirtamyllacisab 50.1 sesu aorez redro- dloh no eht tnemerusaem lennahc tahtsah ton n,edeebt as dedptnuaag aephotr peta tgsnisu eshutoiverp 1 .tnemerusaem 59.0 III. C CE DHFGTONINNUT position (m) 0.9 gnisU eht retlif ngised morf eht suoiverp,noitces dna eht gnidils edom rellortnoc debircsed ni ]6[ na 58.0 suono rrhoctnaylsuamis saw depoleved rof ngis.endoitadilav ehT rotalumis sniatnoc eht raenil-non elcihev ,scimanyd 0.8 40 45 50 55 60 65 suonorhcnysa rosnes sledom htiw tnemerusaem ,esion Time (s) Figure 3. ShortS egmentI n-WaterR esults,P ositionf orR =10 yawaes scimanyd dna eht .CCD gnisU siht rotalumis saa VDA ngised loot eht CCD saw detsujda ot eveihca namumitpo g nsiikl hcafTatorn teet mhneitasrcit ioesfndaihngis .ngised ehT sniag ni htob eht rellortnoc dna retliferew stceffe ot eht noisluporp metsys sa nees ni erugiF .5 ehT doestsujda .te mere wstnemeriuqe recnamrofre ptaht esion dah neeb dettimsnart otni eht rellortnoc gnitluserni ehT ytiliba tescnamrofrep fo eht rotamitse sinwohs ereves noitallicso ni eht relleporp .esnopser esehT hguorht ,noitalumis ees erugiF ,2 ecnis ereht era onlamrof snoitallicso yllautneve dael ot lacinahcem eruliaf fo eht sfoorp ot enimreted eht ytilibats fo denibmoc raenilnon noislupo rmpetsys gnitfahs eud ot eht gniraehs fognitcennoc srotamitse dna .srellortnoc sA ,nees eht rorreecnairavoc pins. slevel lla egrevno cgnitacidni a elbats raenilnon retlif.ngised gnisU eht noitamrofni niatbo gnirud siht tes fosnur emoS fo eht ecnairavoc slevel yam raeppa ot eb oot""hgih dewolla eht retlif sniag ot eb detsujda ot etanimile eht gnivig eht gnileef taht eht retlif si ton ylreporp,dengised noissimsna rfto rosnes esion otni e h.trellortnoc gnisUraenil ,revewoh ngised snoisiced tsum eb edam ot erusne tahteht ngised ,seuqinhcet eht denibmoc rellortnoc retlif refsnart retlif sgal era on oot ,evissecxe dna taht eht rotamitseskcart .dem r sotafuwp trueollep otorutp p VnmDi oAnrofitcnuf well. 0.15 1.0 80.0 0.1 60.0 40.0 0.05 20.0 0 0 (m/s)f u 20.0- -0.05 40.0- fluid velocity estimate (m/s) 60.0- -0.1 80.0- 1.0- 40 45 50 55 60 65 591 002 502 012 512 022 Tim(es ) T(ism)e Figure 4. Short Segment In-Water Results, Fluid Velocity Estimate for Figure 7. Short Segment In-Water Results, Fluid Velocity Estimate for R VDA =10 R VDA =100 008 300 006 200 004 100 002 0 0 002- -100 propeller revs (rpm) propeller revs (rpm) 004- -200 006- -300 008- 195 200 205 210 215 220 04 54 05 55 06 56 Time (s) e)msi(T Figure 8. ShortS egmentI n-WaterR esults,P ropeller RPMs for R =100 Figure 5. ShortS egmentI n-WaterR esults,P ropeller RPMs for R =10 VDA VDA s aehcnamrofr eep h,tC CeD h ftgonin uet h fttolus eer hstA yB gnitsujda eht level fo eht tnemerusaem esion,sretemarap sniatni aC mCe Dh,tero f. eysblAlacitama rddevorpmi noitaunetta fo eht esion otni eht lortnoc metsys saw rellepo rdpecud ehrc uhamt i,wll eywlemert xneoitisop ,dehsilpmocca dna eht esnopser htdiwdnab fo ehtrellortnoc diu ldfetamit see h fteoduting aem hgtnirap m. oeCsnopser saw .desaercni sihT tnemevorpmi ni ycneuqerf esnopserlliw enb a tc ,id7 ns4aerug i,Fsngis eod wet hnteewt esbeiticolev ecuder eht relleporp ,snoitallicso ybereht gniziminim eht ,ecnabruts itdup nfeioduting aemm ae shr totfa hntees .mge n tinssoyUissl ue prfeouohr ltpliaacfin ae hcfcnoeamhc rellepor ptu b,degnahcn udeniame rsa hesnopse rnoitisop yeret ndnoeiMt sneit a sg Caa eCw,hDsteu lnagvi s weeedhnt noislupor peh tf oefi leh tgnisaercn idecude rsa hesnopser gnitse tsih tf ostluse reh T.robraH .8 -s6erug i nFniwo hesra .metsys V. NOITATNEMEL PEMRIAWTFOS 5.3 4.3 noit af sto lsnsoe eirc emhtosheetnriTulopqpcimniu 3.3 X ecnis ti si tilps neewteb eht owt sUPC dellatsni ni.xineohP X 2.3 hat ehT SPN VUA sesu a muitneP desab 401-CP gninnurXNQ dna a CAPSEG draC egaC gninnur 9SO rof noissimlortnoc 1.3 dna .noitucexe ehT CCD seriuqer noitamrofni morfhtob 3 ,srossecorp detcennoc yb tenrehtE ,stekcos ot etupmocdna position (m) 9.2 rellep odrepdnam m eoshcstap .lev enloituce xe ehosttMPR 8.2 ehT lortnoc erutcetihcra yltneserp gninnur ni xineohP si desab no derahs yromem .sessecorp ehT401-CP 7.2 retupmoc snur a ”niam“ ssecorp taht slortnoc eht 6.2 k Cc e Ao ,lePlngihSco nhtEif iwGtoearahtaztahidsnorhcnys 5.2 195 200 205 210 215 220 slortnoc eht emit-laer lortnoc .serutaef ehTsrossecorp-owt Tim(es ) dello r, tryn ero nadfocs eo tefmaesmearuehumhdabmtothcs Figure 6. ShortS egmentI n-WaterR esults,P ositionf orR =100 VDA tnet sri essfniso ncnaorittam r eoesrf heunotrsitonhep aymbes htiw eht kcolc .deeps A lacihparg noitatneserper fosiht sraow rlraenoiti s eonfhpootitai vderdad n. ad etehstTcepxe noitpircs esdi nwohs ni erugiF .9 sA nees ni eht ,cihpargrof .s / 5mnf.coo1itai vderdadn ayttsico ldenvu ohr tg6mi.cw9 eht CCD ,noitatnemelpmi lla dedeen ssecorp era nur nieht 40 1h-tCiP weh teyslon porroufp eChAtPS EsGi ot dneseht 52.0 .srot onmoislupo re p hosttegatl odvednammoc 2.0 51.0 . Os9 Processor QNX Processor *ShaSreegdm Menemtsory ALoop:DV Process 1.0 L Q . . . . N Xo R oe a Wp...dr: iSteen sSoernssor Data to WrR ei atM L dM S e oSR eee SR ola e ndaa ee p d A ns seD R UsV Dop I uo D n a..rDDUrrttaat na:aeiD:t talmiatf leEa r n nOoosdtvmO9 teso sNriof 9 (sa NM .Revi.etiswa.gsod,riyapko ttsnoii STo,irFnrgai, nnl.sa.mtli.e:t)r :ain Process fU_ID…A……RR……prRR…DDDIIIDs___UUVVgggi_Vx WW rr ii ERL ENL nnttdd oo eeDD RR LLoooooopp ee R aa appa e..ddtta:: dR Aaatt DD IM V ooSS e Da D hha sa tu taaa ra e rr P mPo eeeor ntr ddtMMt eef rmm o moo rrI n yyp u t S h a r e d MemoryDavIi g Partioocne sFsilter Process position (m) 5510..00--50.01.0- 0 S eR ne f . . . . tEdaoCrn So ddcoCrm en QowNstLnXortoorpl:oV loV lotlatgaegse *Wr Ri eL t R W E ao M e nLe r dg Se f do Aa i r t oald ot am p l S es tD :Wt C u aea( aE vo r.t e tsn ...aeeFtt.ti Emi,lrotsWemDoe tarianl vist VtFmkeotiasa llWnot _tdae CVa fvcogrlFeneo,it tWmrloa Novt OalFsVvei 9oirl lg_:atVctarig,oe.n . F.i)lterFVaroaerr iSCahbloalewrsint yP ,e Ortnaliyn itnhge tSoh Saurerdg eM Ceomnotrroyl …NAR……N……WWWWW…DaaIDa_avvUVgvaFF_vaaFiiVFllixv__livvuu_rfFlF_FFiVciill_llV__cXXr Co ntU rpU EWL WEpo d VW dn no rla rao d do i tti l R ep teteS......tSS a:et eLLdtata ooS atgaootMeteetEppa Ease::steEsts ue sitr mEteiasmitemtmenaia tanm ttdtfaCO eeroatuo l e mct tupIolnua OpStutuhett apSrhuaetMr dSeedh maMoremreodyr yMemoryave Estimation Filter Process Figure 10. Comparison of Measured and Estimated Position, April 22, 1999, 52.0-2.0- 0 001 002 nuR 003 e)msi(TXX#h3at 004 005 006 Figure 9. Software Implementation of DCC 008 xineoh Peh tn inoitatnemelpm iCC Deh tf omargai dkcol bA eh tstneserpe rmargai dsi h .T 1erugi Fn inevi gsa wVUA 006 e. heTlcih eev h nteirawdr aeh hdt nearawtf oes h ftgonidlem mo ryfticol eevvital eer h,tI DeR hmto r sfyiticol edvnuorg 004 dna VDA eht y , r.or ylganoitcer ie dhmto rf 002 VI. C CED H FTNOOITADIL ALVATNEMIREPXE 0 ehT CCD saw detset ni yeretnoM robraHneewteb propeller revs (rpm) 002- eht shtnom fo hcraM dna yaM .9991 gniruD siht ,emiteht xineohP saw dleh rednu egrus lortnoc rof revo 09,setunim 004- gnirud suoirav ,snur tuohtiw a evird .ffo elbaT 1 sedivorpa elpmas fo eht snur detcudnoc gnirud eht noitadilav foeht 006- 0 001 002 003 004 005 006 007 .rellortnoc Figure 11. Propeller Response, April 22, 1999, Run ) se(miT #3 ecnabruts ie dh,tecnamrofr efeporus a geanminifeD (oita rnoitcejer DRRe h ftnooitaiv eddradna t fsooit aer h st,a) 0.1 di u elnfhfootitai vderdadn a etyohstttico ldenvu oerlgcihev 0.08 r osfecnabruts itdcej eoV r t UnfyAaotili be ah,tyticolev .derapm oen cbascngis eldortn od cnsanoitidn otcnereffid 0.06 ecnabruts itdcefr er po,fnoitinif eR dRe DhogttnirrefeR 0.04 sngis erd oefli h,wor e ozltau q eelbl iRw ReD hntoitallecnac ll iRw ReD h,t] 7,[elcih eev hstetic xteup nliortn oec hetrehw 0.02 dradnat seh t,tnio pgnitarep ohca er o .Fen onah tretaer geb 0 eh ty bdezilamro ns iesnopse rrellepor peh tf onoitaived ground velocity (m/s) ,snoitulov errellepo rmpumixam n . -0.02 max noitceje recnabrutsi dtnellecx etah tsetacidn i1elbaT -0.04 detim iyll neore hswn utrro he shr tonfe v,edeveih csaaw tah tdewoh sstse teh T.dedroce rsa wnoitamrofn ilacitsitats -0.06 0 100 200 300 400 500 600 700 na hrteh teocru oyadsbebruts isd aewlcih eev hnte hnweve Time (s) Figure 12. Measured Ground Velocity, April 22, 1999, Run #3 dednamm oec h otntrut e oretl bsa a tw,iyticol edviu lefht .noihs aeflb a ntnaisoitisop sihT nur saw eht tsom gnitseretni fo ehtnoitadilav s eai rAes fsotlus eer hwto h,s31- 0s1erug i,Fstolp snur .detcudnoc tA eht gninnigeb fo siht ,nur ti sawdeciton lirp An odetcudno csa wnu rsi h .Tsnu rnoitadila veh tf oeno taht eht draobrats tfahs saw ton .gninrut nevE htiwsiht dednamm os caxwine o .he rPhoTbr ayHeretn on9Mi9 9,122 noisluporp metsys ,ytlausac eht elcihev saw elba otdloh lanidutigno leh tn isrete m0f onoitiso planoitagiva naot noitisop dna eht rellortnoc did ton og .elbatsnu htiWylno s adevahe belcihe veh t,etacidn istluse reh ts .Anoitcerid eno tfahs gninrut eht evitceffe tupni niag rof eht elcihevsaw decuder yb .%05 snoitarepO fo siht erutan etacidni ayrev 1 tsubor .ngised tI nac eb nees ni erugiF ,11 taht ereht sia 9.0 llams esaercni ni relleporp snoitulover dnuora eht 05dnoces Single tniop fo eht .nur ataD sisylana detacidni taht siht saw /u)gf 8.0 operations shaft Shortruns 7.0 yletamixorppa nehw eht draobrats tfahs .deliaf noitagitsevnI 6.0 otni eht esuac fo eht tfahs er udleinaifmret etdaht alasrevinu .esoo ldekro wda hgnitfah stah tn itnioj 5.0 4.0 h = 10 2.0 disturbance rejection ratio (u 3.0 h = 25 2.0 h = 50 Simulation 51.0 using harbor 1.0 disturbance 1.0 00 50.0 1.0 51.0 2.0 52.0 3.0 53.0 4.0 normalized input 50.0 Figure1 4.C omparisono fD CCP erformance,S imulationa ndE xperimental 0 ehT sesnopser deyalpsid ni erugiF 61 era rofeht rotaluger .noitulos tahW si tnaem yb ,siht gnillacer tahteht 50.0- fluid velocity (m/s) CMS noitalumrof seriuqer yllacitamenik tnetsisnoc,noitisop 1.0- yticolev dna ,noitarelecca si taht on dnammoc ,stupnirehto taht noitisop erew .desu nI gniod ,siht ti si detcepxe tahteht 51.0- elcihev lliw revo toohs dna etallicso dnuora ehtdednammoc 2.0- .em igtniltt eesm ohst itwnetsisn oncoitisop 0 001 002 003 004 005 006 ) se(miT Figure 13. Fluid Velocity Estimate, April 22, 1999, Run #3 1.4 sA a lacihparg noitatneserper fo ehtecnamrofrep 1.2 detcepxe rof eht CCD a suo isrnaovitalumi seraw,detcudnoc gnisu eht suonorhcnysa rotalumis dessucsid ,reilrae htiweht 1 detamitse diulf yticolev deniatbo gnirud siht nur lirpA(,22 0.8 ,9991 run # . e)c3n a sba r eueshthntsTi iandgo eh tCCeDrew h 0 1 = h 5 2 = deirav ot ecudorp a noitisop esnopser sesrev relleporp 0.6 h 0 5 = Position (m) es n.oepvsreurc dl,e astettnh nleTleusamsenuieritrrpceapxe 0.4 el bdae Ts,o 1 pl dn maeeoeicrnvrie iretweaupehtcurtbsooeht morf .noitalumis esehT stluser era nwohs ni erugiF .41 sA 0.2 ,nees eht latnemirepxe dna laciteroeht stluser era yrevesolc .rotal ucmiitssi lyalelra cgin siaythapcidni 00 100 200 300 400 500 600 700 )s (emiT ehT snosirapmoc deyalpsid ni erugiF ,41 dleiy Figure 15. Comparison of Transient Response for Various Control Gains thgisni otni eht elbaveihca ecnamrofrep fo eht .CCD tI setacidni taht ereht si a timil ot eht tnuoma foecnabrutsid htiW eseht tneisnart sesnopser emoc emosseussi noit cteajhe tr.e ly blsalizaicli aseyrhp sih Ttimild eslilortnoc htiw drager ot lanoitarep o.noitatnemelpmi fI eht laog siot yb ytiliba fo eht noisluporp metsys ot ecudorp ehtdedeen n eoe lise ctoh,iiltohs c teot, vpughb,nt tiintchwauecojuboot .s enhoTitalli cesvoisse ctxueoh tniowiti snoipatn itoautmpni emos snaem fo gnitciderp eht tneisnart tsum eb .elbaliava A evisse csxneoitallicso evah a latnemirted tceffe fo eht efilfo gnitluser noitseuq taht sdeen ot eb derewsna ;si seoDeht .met snyosislup oerhpt depoleved ,rotalumis ,hcihw desab no eht nosirapmocni sA a ,eton eht trohs ,snur deyalpsid ni erugiF,41 erugiF ,41 yletarucca tciderp eht tneisnart ?esnopser yB gnirapmoc eht stluser fo eht latnemirepxe snur dna eht froetemar anpi argellort n hoat cidwetcudn oecrew h ,a 0 0=1 detalumis ,stluser rof eht emas ecnabrutsid tupni dnaCCD hgih .niag fI eht htgnel fo eseht snur erew ,dednetxeeseht ."se y,"rews nena bancoitse ue qh, t6e1rug ie Fe,sngised stniop dluow evom resolc ot eht evruc sa htiw eht rehtosnur sA nees ni siht ,tolp eht detalumis stluseretarucca .deyalpsid tcelfer eht derusaem tneisnart esnopser fo eht .xineohP ehT pU ot siht ,tniop eht ylno stluser detneserp erarof ydaets etats ,esnopser ,revewoh seod ton .hctam ehTnosaer eht xineohP gniniatniam noitisop ot eht ,nigiro ehttniop rof siht si .dlof-owt ,tsriF eht ,xineohP rof yrevocer,snosaer hcihw eht nur saw .detaitini snoitseuQ esira sa otwoh si deniatniam yletamixorppa sdnuop-owt .tnayoub sihT evitceffe eht rellortnoc si ni gnilaed htiw .stneisnart sihT thgiew dna ycnayoub hctamsim esuac lanoitiddanoitaticxe noitseuq yam eb derewsna yb gnirrefer ot erugiF .51 sihT secrof gnitluser morf eht evaw decudni diulf.snoitarelecca erugif stciped eht tneisnart esnopser fo eht xineohP rofeht ecniS eht diulf noitarelecca tonnac eb ,derusaem siht suoirav CCD sniag detneserp ni erugiF .41 sA ehterugif lanoitidda noitaticxe ecrof si tluciffid ot etacilper ni .ll eywlemert xsetneisna rhtt iswla erdellortn oec h,tsetacidni noitalumis gnidleiy srorre neewteb eht laer dnadetalumis .esnopser ,dnoceS eht latnemirepxe stluser eraderusaem ",SV UtAsoC Proceedings of the IEEE Symposium morf a F Od Di, 6gyeidrroe bh swsrtaole tumeasohlecturmis on Autonomous Underwater Vehicle Technology, morf a FOD1 egrus .ledom ehT gnilpuoc stceffe morfeht .8 9t9s1u g ,u,AAeMgdirbmaC surge .nosirap m eothccte flf lesicwima nhycdtip- 6. ,.J .,Ayela ed Hn,a.S .,JledeiR “ret awWollahS 1.2 it lgunM iss VUgfUnOAip eneoKitatS rosneS- dnnoAitcid eercPnabrut sei vD ranoWoFisuF 1 ”,noitasnepmoC IEEE Proceedings of Oceans ’98, .8 9r9e1bmet p,eeScn a,reFciN 0.8 XX ASIUMV 7. ,.S. J,ledeiR Seaway Learning And Motion 0.6 Compensation In Shallow Waters For Small AUVs, ,loo hectSaudargt sloaP v,anNoitatres s.iDD.hP position (m)0.4 .9 9e 9 n,1,uAyJCeretnoM 0.2 0 -0.2 0 100 200 300 400 500 600 700 Time (s) Figure 16. Transient Response Prediction of the DCC YRAMMUS .IIV we n af onoitadila vdn anoitatnemelpm i,ngise dehT )CC Dr(ellortn onCoitasnepm oeCcnabrutsiD n eseabh den uytlrepo rgapni s yutba hettacid nsitlus e er. hdTetneserp mrofr eospttob orretawred ntunegillet nfyiotili be ah,tmetsys ,rehta go tytilib arieh ty bdevorpm ier asksa tnoitnevretni .tnemnoriv ngenikr orwie httuo bnaoitamrof ne isd unnarael ,elbalia vsyaitiliba tr soffoo rlpamr oohfnguohtlA sCi CeD htta hdtetartsnom eedv ashnoitalum issuonorhcnysa nfooitami t dsgnenaikc aerltbatpe csceadiv o drenplabats e htta hdtetadil asv akhr o ew. hsTtup neicnabruts idd neatats lae raf onoitatnemelpm idn atnempoleved deddeb meemit- ll a rm)osCf CrDe(llort nnooCitasnep meocCnabrutsiD s titia hgtniwo hysgolonhc ewt edanedivo rdp n,asVUA sks agtnipeek-noita tr sosfelcih ervetawred ne usoeutlbissop .r ewtoal wlnaihs TNEMGDELWONKCA I dluow osla ekil ot ezingocer eht laicnanif troppus foeht eciffO fo lavaN hcraeseR .rM( moT )niatruC rednutcartnoc .oN .57103RW8941000N SECNEREFER 1. ,. K,atagO Modern Control Engineering-ecitn e,rP .0991 ,llaH 2. ,,.bAleG Applied Optimal Estimation,sse rT PI,M . 4,,7eA9gM1dirbmaC 3. d n .a,Ct i eWsdadunaC ,eJnitolS gnidi l,S.“E.J-. ”,srotalupin atMob orR osfrevresbO Automatica .,v .468-95 8.p p,199 1,5. n,72 4. rsorfevres b ,O,.“iRnamajaR raenil nzotNihcspiL ”,smetsys IEEE Transactions on Automatic Control, .104-79 3.p p,899 1hcra M, 3 n,34v 5. ,n A;.J. A,yelaeH en in LO;".B .,Docr ad Mn;a.P.E w or LosFa irBosn egSnida efnHOoitasnepmoC # ss Date Run Length DRR /n comments n max 99/2/4 4 4nim 4263.0 2.96 ,niag hgih runshort 5 4nim 4236.0 3.08 ,niag hgih ,nur trohs lacisyhp elcihev debrutsid 6 4nim 2134.0 0.265 ,niag hgih runshort 8 4nim 0905.0 0.285 ,niag hgih runshort 99/22/4 3 01nim 8055.0 0.108 elgni s,nia ghgih tfahs 99/52/5 6 01nim 0263.0 0.192 , nhigaigh-muidem es iVoDnA problem 8 01nim 8793.0 0.126 ,niag wol-muidem es iVoDnA problem 9 01nim 7594.0 0.083 VDA ,niag wol mel beosripon 11 01nim 7853.0 0.202 , nhigaigh-muidem es iVoDnA problem 12 01nim 6724.0 0.144 ,niag wol-muidem es iVoDnA problem elbaT 1 Sample Summary of DCC Validation Runs
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