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2 Chaptr 3 A Hybrid of Fuzzy and Fuzzy Slf-Tuning Controllr for Srvo Elctro-Hydraulic Systm Kwanchai Sinthipsomboon, Issar Hunsacharoonroj, Jospt Khdari, Watcharin Po-ngan and Pornjit Pratumsuwan Additional information is availabl at th nd of th chaptr Introduction Th application of hydraulic actuation to havy duty quipmnt rflcts th ability of th hydraulic circuit to transmit largr forcs and to b asily controlld. It has many distinct advantags such as th rspons accuracy, slf-lubricating and hat transfr proprtis of th fluid, rlativ larg torqus, larg torqu-to-inrtia ratios, high loop gains, rlativly high stiffnss and small position rror. Although th high cost of hydraulic componnts and powr unit, loss of powr du to lakag, inflxibility, nonlinar rspons, and rrorpron low powr opration tnds to limit th us of hydraulic drivs, thy nvrthlss constitut a larg subst of all industrial drivs and ar xtnsivly usd in th transportation and manufacturing industris (Mrrit, 976; Rong-Fong Fung t al, 997; Aliyari t al, 27). Th Srvo Elctro-hydraulic Systm (SEHS), among othrs, is prhaps th most important systm bcaus it taks th advantags of both th larg output powr of traditional hydraulic systms and th rapid rspons of lctric systms. Howvr, thr ar also many challngs in th dsign of SEHS. For xampl, thy ar th highly nonlinar phnomna such as fluid comprssibility, th flow/prssur rlationship and dad-band du to th intrnal lakag and hystrsis, and th many uncrtaintis of hydraulic systms du to linarization. Thrfor, it sms to b quit difficult to prform a high prcision srvo control by using linar control mthod Rong-Fong Fung t al, 997; Aliyari t al, 27; Pratumsuwan t al, 2). Classical controllr is th most popular control tool in many industrial applications bcaus thy can improv both th transint rspons and stady stat rror of th systm at th sam tim. Morovr, it has simpl architctur and concivabl physical intuition 22 Sinthipsomboon t al., licns InTch. This is an opn accss chaptr distributd undr th trms of th Crativ Commons Attribution Licns ( which prmits unrstrictd us, distribution, and rproduction in any mdium, providd th original work is proprly citd.

3 3 Fuzzy Controllrs Rcnt Advancs in Thory and Applications of its paramtr. Traditionally, th paramtrs of a classical controllr, i.. KP, KI, and KD, ar usually fixd during opration. Consquntly, such a controllr is infficint for control a systm whil th systm is disturbd by unknown facts, or th surrounding nvironmnt of th systm is changd (Panichkun & Ngachronkul, 2; Pratumsuwan t al, 2). Fuzzy control is robust to th systm with variation of systm dynamics and th systm of modl fr or th systm which prcis information is not rquird. It has bn succssfully usd in th complx ill-dfind procss with bttr prformanc than that of a controllr. Anothr important advanc of fuzzy controllr is a short ris tim and a small ovrshoot (Aliyari t al, 27; Panichkun & Ngachronkul, 2). Howvr, controllr is bttr abl to control and minimiz th stady stat rror of th systm. To nhanc th controllr prformanc, hybridization of ths two controllr structurs coms to on mind immdiatly to xploit th bnficial sids of both catgoris, know as a hybrid of fuzzy and controllr (Panichkun & Ngachronkul, 2; Pratumsuwan t al, 2). Nvrthlss, a hybrid of fuzzy and dos not prform wll whn applid to th SEHS, bcaus whn th SEHS paramtrs changs will rquir nw adjustmnt of th gains. A hybrid of fuzzy and fuzzy slf-tuning controllr is proposd in this papr. Th proposd control schm is sparatd into two parts, fuzzy controllr and fuzzy slf-tuning controllr. Fuzzy controllr is usd to control systms whn th output valu of systm far away from th targt valu. Fuzzy slf-tuning controllr is applid whn th output valu is nar th dsird valu. In trms of adjusting th gains tuning using fuzzy as to obtain an optimum valu. 2. Srvo lctro-hydraulic systm Th physical modl of a nonlinar srvo lctro-hydraulic systm is shown in Figur. m J t G T d Hydraulic Motor Srvo valv Load Figur. Th physical modl of a srvo lctro-hydraulic systm. Th inrtial-damping with a nonlinar torsional spring systm is drivn by a hydraulic motor and th rotation motion of th motor is controlld by a srvo valv. Highr control input voltag can produc largr valv flow from th srvo valv and fast rotation motion of

4 A Hybrid of Fuzzy and Fuzzy Slf-Tuning Controllr for Srvo Elctro-Hydraulic Systm 3 th motor. Th ntir systm quations ar dscribd as follows. Th srvo valv flow quation () is dscribd as: Q K x K P () L q v c L whr QL is th load flow, Xv is th displacmnt of th spool in th srvo valv, Kc is th flow-prssur cofficint, PL is th load prssur, and Kq is th flow gain which varis at diffrnt oprating points. Kq is givn by K q ( P P )sgn( X ) S L v C w (2) d whr Cd is th discharg cofficint, w is th ara gradint, is th fluid mass dnsity, and PS is th supply prssur. Th continuity quation to th motor is formulatd as Vt Q D CP P (3) L m m l L L 4 whr Dm is th volumtric displacmnt, is th angular vlocity of th motor shaft, Cl is m th total lakag cofficint of th motor, Vt is th total comprssd volum, and is th ffctiv bulk modulus of th systm. Substituting () into (3) lads to Vt KX D KP P q v m m l L L 4 (4) whr Kl=Kc+Cl is th total lakag cofficint of th hydraulic systm. Th torqu balanc quation for th motor is dscribd as follows: PD J B G G T 3 ( ) L m t m m m m n m d (5) whr Jt is th total inrtial of motor and load, Bm is th viscous damping cofficint of th 3 load, Td is th disturbanc of th systm, and G is th nonlinar stiffnss of th spring. From () to (5), th hydraulic srvomchanism systm quation can b dscribd by a stat quation as follows: n m X X X X 3 X ax bu N( X, t) d( t) i Z rx i i (6)

5 32 Fuzzy Controllrs Rcnt Advancs in Thory and Applications whr X() t X () t X () t X () t () t () t () t 4 K a () t V J T T 2 3 m m m t t m lt D K G a () t B 2 m V J V J J 4 a () t K 3 V 4 D bx ( ) V J lt t t t t t t t lt t m G B J q m t KK V 4 KG 3G Nxt (, ) GX GXX VJ J 4 K lt dt () T T d d V J J lt n 3 n 2 2 t t t t t t in which N(X,t) rprsnts th nonlinar trms of th systm. 3. Systm dscriptions W ar considring a PC-basd spd control of th SEHS that will us ithr a hybrid fuzzy or a hybrid of fuzzy and fuzzy slf-tuning controllr. Th motor spd of this systm is controlld. In ordr to construct fair tst cas for comparing both controllrs, th xprimnts ar constructd basd on th sam hardwar lmnts. Th spcifications of this systm ar dpictd in Tabl and Figur 2 rspctivly. Figur 2. Exprimntal Stup.

6 ?. B a r A Hybrid of Fuzzy and Fuzzy Slf-Tuning Controllr for Srvo Elctro-Hydraulic Systm 33 Elmnts Dscriptions Hydraulic Motor Gomtric displacmnt 9.9 cm 3 Max. Spd rpm, Max. torqu 25Nm Max. prssur drop bar Max. oil flow 2l/min Proportional valv dirctly actuatd spool valv, grad of filtration m, nominal flow rat 6l/min (at pn = 6.9 bar/control dg), nominal currnt 6 ma, rpatability < %, hystrsis <5% Pump (supply prssur) bar Amplifir card st point valus 5 VDC, solnoid outputs (PWM signal) 24 V, dithr frquncy 2 Hz, max powr 45W. Encodr 8 c/t, I(optical shaft ncodr) DAC Rsolution 5 bit DAC, output -V DAQ Card NI 622 PCI analog input rsolutions 6 bits (input rang V), output rsolutions 6 bits (output rang V), 833 ks/s (6 s fullscal sttling) Oprating systms & Windows XP, and LabVIEW 8.6 Program Tabl. Spcifications of th SEHS. 4. Controllr dsigns A closd loop systm, whithr th rfrnc signal is st manually or automatically, can prform control of motor spd. Figur 3 rprsnts typical of an Automatic Closd Loop control systm. As shown in th figur, th vlocity of a hydraulic motor is controlld by a srvo valv. Th srvo valv solnoid is rciving driving lctrical currnt from an amplifir card, which is gnrating th driving currnt basd on a control signal supplid by a controllr. Th controllr rsponsibility is to continuously compar th rfrnc signal and th actual motor spd fdback by th vlocity snsor, aftr consquntly gnrat th adquat control signal. Motor & Snsor Hybrid of Fuzzy and Fuzzy slftuning Controllr Amplifir Srvo Valv Powr Supply Powr Unit Figur 3. Block diagram of using a hybrid fuzzy and fuzzy slf-tuning controls th SEHS.

7 34 Fuzzy Controllrs Rcnt Advancs in Thory and Applications Thr ar various typs of control systm usd in classical control, modrn control and intllignt control systms, ach having bn studid and implmntd in many industrial applications. Evry control systm mthod has its advantags and disadvantags. Thrfor, th trnd is to implmnt hybrid systms consisting of mor than on typ of control tchniqu. 4.. controllr Th control mthod has bn widly usd in industry during last svral dcads bcaus of its simplicity. Th implmntation of control, as shown in (7), rquirs finding suitabl valus for th gain paramtrs KP, KI, and KD. To tun ths paramtrs, th modl is linarizd around diffrnt quilibrium points, whr (k) is th rror signal. k uk ( ) Kk ( ) K i ( ) K k ( ) k ( ) P D (7) I i KP, KI, KD y m u yp SEHS Figur 4. Block diagram of a controllr. Howvr, th mthod is not suitabl for controlling a systm with a larg amount of lag, paramtr variations, and uncrtainty in th modl. Thus, control cannot accuratly control vlocity in a SEHS (Rong-Fong Fung t al, 997; Aliyari t al, 27) Fuzzy controllr Fuzzy Control (FC) has th advantag that it dos not rquir an accurat mathmatical modl of th procss. It uss a st of artificial ruls in a dcision-making tabl and calculats an output basd on th tabl (Aliyari t al, 27; Panichkun & Ngachronkul, 2). Knowldg Rul bas Databas In Fuzzification Infrnc Engin Dfuzzification Out Figur 5. Structur of FC.

8 A Hybrid of Fuzzy and Fuzzy Slf-Tuning Controllr for Srvo Elctro-Hydraulic Systm 35 Figur 5 & 6 show a schmatic diagram of a fuzzy control systm. Input variabls go through th fuzzification intrfac and ar convrtd to linguistic variabls. Thn, a databas and rul bas holding th dcision-making logic ar usd to infr th fuzzy output. Finally, a dfuzzification mthod convrts th fuzzy output into a signal to b snt out. First, th two input variabls must b dfind in trms of linguistics. Th rror () in vlocity is xprssd by a numbr in th intrval from - to. Thr ar fiv linguistic trms of th rror in vlocity: ngativ big (NB), ngativ (N), zro (Z), positiv (P), and positiv big (PB). Similarly, th fuzzy st of th rror chang of th vlocity or acclration () is prsntd as {NB, N, Z, P, PB} ovr th intrval from - to V. Finally, th fuzzy st of th output signal is prsntd as {NB, N, Z, P, PB} ovr th intrval from -5 to 5V. y m FC u yp SEHS Figur 6. Block digram of a FC. Th knowldg bas for a fuzzy controllr consists of a rul bas and mmbrship functions. It is rasonabl to prsnt ths linguistic trms by triangular-shap mmbrship functions, as shown in Figur 6. A fuzzy control knowldg bas must b dvlopd that uss th linguistic dscription of th input variabl. In this papr, an xprt s xprinc and knowldg mthod is usd to build a rul bas (Zhang t al, 24). Th rul bas consists of a st of linguistic IF-THEN ruls containing two antcdncs and on consqunc, as xprssd in th following form: R : IF A and B THEN u C, (8) ijk,, i j k whr i 5, j 5, and k 5. Th total numbr of IF-THEN ruls is 25 and is rprsntd in matrix form, calld a fuzzy rul matrix, as shown in Tabl 2. Th dcision-making output can b obtaind using a max-min fuzzy infrnc whr th crisp output is calculatd by th cntr of gravity (COG) mthod. Tabl 2. Fuzzy Ruls of a FC. NB N Z P PB NB NB NB N N Z N NB N N Z P Z N N Z P P P N Z P P PB PB Z P P PB PB

9 36 Fuzzy Controllrs Rcnt Advancs in Thory and Applications NB N Z P PB NB N Z P PB NB N Z P PB Output (u) Figur 7. Fuzzy sts of a FC. Output Figur 8. Input-output mapping of a FC Hybrid of fuzzy and controllr Whil convntional controllrs ar snsitiv to variations in th systm paramtrs, fuzzy controllrs do not nd prcis information about th systm variabls in ordr to b ffctiv. Howvr, controllrs ar bttr abl to control and minimiz th stady stat rror of th systm. Hnc, a hybrid systm, as shown in figur 9, was dvlopd to utiliz th advantags of both controllr and fuzzy controllr (Parnichkul & Ngacharonkul, 2; Ernoglu t al., 26; Pratumsuwan t al., 29;). Figur 9 shows a switch btwn th fuzzy controllr and th controllr, whr th position of th switch dpnds on th rror btwn th actual valu and st point valu. If th rror in vlocity rachs a valu highr than that of th thrshold, th hybrid systm applis th fuzzy controllr, which has a fast ris tim and a small amount of ovrshoot, to th systm in ordr to corrct th vlocity with rspct to th st point. Whn th vlocity is blow th thrshold or clos to th st point, th hybrid systm shifts control to th, which has bttr accuracy nar th st vlocity (Parnichkul & Ngacharonkul, 2; Ernoglu t al., 26; Pratumsuwan t al., 29;).

10 A Hybrid of Fuzzy and Fuzzy Slf-Tuning Controllr for Srvo Elctro-Hydraulic Systm 37 Slctor Controllr? yp y m Fuzzy Controllr FC u SEHS Figur 9. Block diagram of a hybrid fuzzy controllr Fuzzy slf-tuning controllr Fuzzy slf-tuning controllr mans that th tr paramtrs KP, KI, and KD of controllr ar tund by using fuzzy tunr (Zhang t al, 24; Song & Liu, 2; Zulfatman & Rahmat, 26; Fng t al, 29). Th cofficints of th convntional controllr ar not oftn proprty tund for th nonlinar plant with unprdictabl paramtr variations. Hnc, it is ncssary to automatically tun th paramtrs. Th structur of th fuzzy slf-tuning controllr is shown in Figur. Whr is th rror btwn dsird vlocity st point and th output, is th drivation of rror. Th paramtrs ar tund by using fuzzy tunr, which provid a nonlinar mapping from and of rror to paramtrs. Fuzzy tunr KP KI KD St-point Controllr SEHS Output Figur. Block diagram of a fuzzy slf-tuning controllr. Rgarding to th fuzzy structur, thr ar two inputs to fuzzy infrnc: and, and thr outputs for ach controllr paramtr KP, KI, and KD rspctivly. Mamdani modl is applid as structur of fuzzy infrnc with som modification to obtain th optimum valu for KP, KI, and KD. Suppos th variabl rangs of th paramtrs of controllr ar [KPmin, KPmax], [KImin, KImax], and [KDmin, KDmax] rspctivly. Th rang of ach paramtrs was dtrmind basd on th xprimntal on controls th SEHS. Th rang of ach paramtrs ar, KP[8,5], KI[.3,.], and KD[.,.]. Thrfor, thy can b calibratd ovr th intrval [,] as follows:

11 38 Fuzzy Controllrs Rcnt Advancs in Thory and Applications K K K 8 P Pmin P K P, K P 7 K P 8 K K 5 8 Pmax Im ax Dmax Pmin K K K.3 I Imin I K I, K I.7 K I.3 K K..3 Im in K K K. D Dmin D K, K.9K. D D D K K.. Dmin Th mmbrship functions of ths inputs fuzzy sts ar shown in Figur 8. Th linguistic variabl lvls ar assignd as: ngativ big (NB), ngativ (N), zro (Z), positiv (P), and positiv big (PB). Similarly, th fuzzy st of th rror chang of th vlocity or acclration () is prsntd as {NB, N, Z, P, PB}. Ths lvls ar chosn from th charactristics and spcification of th SEHS. Th rangs of ths inputs ar from - to. Finally, whras th mmbrship functions of outputs KP, KI, and KD ar shown in Fig. 8. Th linguistic lvls of ths outputs ar assignd as: ngativ big (NB), ngativ (N), zro (Z), positiv (P), and positiv big (PB) similarly whr th rangs from to. Tabl 3. Fuzzy Ruls of KP Gain. NB N Z P PB NB NB NB NB N Z N NB N N N Z Z NB N Z P PB P Z P P P PB PB Z P PB PB PB Tabl 4. Fuzzy Ruls of KI Gain. NB N Z P PB NB PB PB PB N NB N PB P P Z NB Z P P Z N NB P Z P N N NB PB Z N NB NB NB Tabl 5. Fuzzy Ruls of KD Gain. NB N Z P PB NB NB NB NB P PB N NB N N Z PB Z N N Z P PB P Z N P P PB PB Z P PB PB PB

12 A Hybrid of Fuzzy and Fuzzy Slf-Tuning Controllr for Srvo Elctro-Hydraulic Systm 39 NB N Z P PB NB N Z P PB NB N Z P PB Output KP NB N Z P PB Output KI NB N Z P PB E-6 2E-6 4E-6 6E-6 8E-6 E-5 Output KD Figur. Fuzzy sts of a fuzzy slf-tuning controllr Hybrid of fuzzy and fuzzy slf-tuning controllr A hybrid of fuzzy and fuzzy slf-tuning controllr, as shown in Figur 2, was dvlopd to combin th advantags of both fuzzy and controllr togthr. In addition, th adjustmnt gain of with a fuzzy tunr is includd to purposd controllr also, which all of ths dscribd in sction 4., 4.2, 4.3, and 4.4.? Fuzzy tunr St-point KP KI KD Controllr Slct SEHS Output Fuzzy Controllr Figur 2. Block diagram of a fuzzy slf-tuning controllr.

13 3 Fuzzy Controllrs Rcnt Advancs in Thory and Applications 5. Th xprimntal rsults Th ffctivnss of th proposd hybrid of fuzzy and fuzzy-tun controllr is valuatd xprimntally with th SEHS and is compard with that of th hybrid fuzzy controllr which uss th nominal valus of th gains obtaind by xprimnt. Th control algorithms dscribd in sction 4., 4.2, 4.3, and 4.4 wr hybridizd and applid to th SEHS using by LabVIEW program as th dvlopmnt platform and shown in Figur 3. Figur 3. Th control algorithms ar dvlopd by LabVIEW program. Th proposd of a hybrid of fuzzy and fuzzy slf-tuning controllr is valuatd xprimntally with th motor spd control of SEHS and is compard with that of th convntional of a hybrid of fuzzy and controllr. For th first xprimnt to obsrv th rspons of th SEHS control output of th both controllr, which shown in Figur. 4 and Tabl 6, rspctivly. Thn, chang th paramtrs of th SEHS, bcaus xisting xprimntal st is difficult to chang th load so that this chang in prssur of th SEHS instad. Th chang in prssur will mak many valus, but th paramtrs of th both controllr still us th original stting from th prvious first. Figur 4, Tabl 6, and Figur 5, Tabl 7 show xampls of th rsponss of th output of th both controllr, which rsultd from changing th original valu of systm prssur ar 5 bar and bar prssur. Howvr, all ths xprimnts th valu of which is usd as a rfrnc in th slction of a controllr is st at.92 that is th optimum valu from xprimnt. Whn th xprimnt has changd th paramtrs of th SEHS will find that th hybrid of fuzzy and fuzzy slf-tuning would lad to a satisfactory rspons ovr th hybrid of

14 A Hybrid of Fuzzy and Fuzzy Slf-Tuning Controllr for Srvo Elctro-Hydraulic Systm 3 fuzzy and controllr. This is bcaus th proposd controllr dos not rquir to adjustmnt th nw paramtrs of controllr although th paramtrs of th SEHS will chang. Figur 4. Comparison of th rsults of th fiv controls whn th prssur was st at 5 bar Controllr Vlocity (rpm) Rsults Vlocity (rpm) Output Error MO PO.. F % Ovrshoot v Ris tim (Tr)(s) Tim dlay (Td)(s) Sttling Tim (Ts)(s) Fuzzy Hybrid Fuzzy Fuzzy Slftuning Hybrid Fuzzy and Fuzzy slftuning Tabl 6. Comparison of th rsults of th fiv controls whn th prssur was st at 5 bar.

15 32 Fuzzy Controllrs Rcnt Advancs in Thory and Applications Figur 5. Comparison of th rsults of th fiv controls whn th prssur was st at bar Controllr Vlocity (rpm) Rsults Vlocity (rpm) Error Output MO PO.. F % Ovrshoot v Ris tim (Tr)(s) Tim dlay (Td)(s) Sttling Tim (Ts)(s) Fuzzy Hybrid Fuzzy Fuzzy Slftuning Hybrid Fuzzy and Fuzzy slftuning Tabl 7. Comparison of th rsults of th fiv controls whn th prssur was st at bar. 6. Conclusions Th objctiv of this study, w proposd th hybrid of fuzzy and fuzzy slf-tuning controllr for motor spd control of a SEHS. Th proposd control schm is sparatd into two parts, fuzzy controllr and fuzzy slf-tuning controllr. Fuzzy controllr is usd to control systms whn th output valu of systm far away from th targt valu. Fuzzy slftuning controllr is applid whn th output valu is nar th dsird valu. In th trms of adjusting th paramtrs ar tund by using fuzzy tunr as to obtain th

16 A Hybrid of Fuzzy and Fuzzy Slf-Tuning Controllr for Srvo Elctro-Hydraulic Systm 33 optimum valu. W dmonstrat th prformanc of control schm via xprimnts prformd on th motor spd control of th SEHS. Th rsults from th xprimnts show that th proposd a hybrid of fuzzy and fuzzy slf-tuning controllr has suprior prformanc compard to a hybrid of fuzzy and controllr. This is bcaus th proposd controllr dos not rquir to radjustmnt th paramtrs of controllr although th paramtrs of th SEHS will chang any. Author dtails Kwanchai Sinthipsomboon, Issar Hunsacharoonroj and Jospt Khdari, Rajamangala Univrsity of Tchnology, Rattanakosin, Thailand Watcharin Po-ngan and Pornjit Pratumsuwan King Mongkut s Univrsity of Tchnology North Bangkok, Thailand Acknowldgmnt Th authors would lik to thank USE FLO-LINE Co., Ltd. and mchatronics ducational rsarch group for thir quipmnts and tchnical support of this rsarch projct. 7. Rfrncs Mrrit, H.E., Hydraulic Control Systm. John Wily, Nw York, 976. Rong-Fong Fung, Yun-Chn Wang, Rong-Tai Yang, and Hsing-Hsin Huang., A variabl structur control with proportional and intgral compnsatios for lctrohydraulic position srvo control systm, Mchatronics vol.7, no., 997, pp M. Aliyari, Shoorhdli, M. Tshnhlab, and Aliyari Shoorhdli., Vlocity control of an lctro hydraulic srvosystm, IEEE, 27, pp Parnichkun, M. and C. Ngacharonkul., Hybrid of fuzzy and in kinmatics of a pnumatic systm, Procding of th 26th Annual Confrnc of th IEEE Industrial Elctronics Socity, Japan, 2, pp: Pornjit Pratumsuwan, Siripun Thongchai, and Surapan Tansriwong., A Hybrid of Fuzzy and Proportional-Intgral-Drivativ Controllr for Elctro-Hydraulic Position Srvo Systm Enrgy Rsarch Journal, vol.,issu 2, 2, pp Jianming Zhang, Ning Wang, and Shuqing Wang., Dvlopd mthod of tuning controllrs with fuzzy ruls for intgrating procsss, Procding of th 24 Amrican control Confrnc, Massachustts, 24, pp Shoujun Song and Wiguo Liu., Fuzzy paramtrs slf-tuning control of switchd rluctanc motor basd on Simulink/NCD, CIMCA-IAWTIC 6, IEEE, 26. Zulfatman and M.F. Rahmat.., Application of slf-tuning fuzzy controllr on industrial hydraulic actuator using systm idntification approach, Intrnational journal on amart snsing and intllignt systm, vol. 2, no. 2, 29, pp

17 34 Fuzzy Controllrs Rcnt Advancs in Thory and Applications Bin Fng, Guofang Gong, and Huayong Yang., Slf-tuning paramtr fuzzy tmpratur control in a larg hydraulic systm, Intrnational Confrnc on Advancd Intllignt Mchatronics, IEEE/ASME,29, pp.48-42