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IR6. Cheryl and John Neerhout, Jr., Distinguished Professor.
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Japan Society of Applied Sciences Meeting 12118/08
. Early successful example
r Current Research Projects
. Kinematic Kalman Filter (KKfl for End-Effector
Sensing of Robotic Manipulat6rs
. Controller tuninq of drive trains with auxiliaru
load-side acceleTometers
. Smaft shoes for human walking gait phase
detection.
. Flexible joint actuator for human rehabilitation
' Concluding Remarks 2
,, Synergy in terms of
*----
. Dealing with complexity
. Performance
. Physicaldimension
. Cost
. Power elficiency
r Time for development
r and so on.
Modern Mechatronic Systern
Mechatronics - Y2K Definition
The synergetic integration of
physical systems with
electronics/i nformation technology
and complex-decision making (in
the design, manufacture and
operation of industrial products
and processes).
Control Theory in Mechatronics:
Necessary but not Sufficient
While controltheory plays a critical role in
the synthesis of mechatronic systems, it
is but a part of the synergy achieved by
modeli ng, sensing/actuation
technologies and decision-making
functions.

7. ,Successful application of control theory
.*k. to mechatronic problems require:
=G-
r to understand and formulate the
problem right,
' to choose appropriate control theory,
and
. to select suitable sensing/actuation
technologies
for synergistic integ ration.
. Early successful example
r Current Research Projects
. Kinematic lGlman Filter (KKD for End-Effector
Sensing of Robotic Manipulatbrs
. Controller tuninq of drive trains with auxiliarv
load-side acceleiometers
. Smart shoes for human walking gait phase
detection.
. Flexible joint actuator for human rehabilitation
. Concluding Remarks e
Co.ntrel of $,tleehanieal S.v.ster,ns
Noncircular Machining/Repetitive Control
I-q Tsao and M. Tomizuka, "Adaptive and Repetitive Cmtrol Algorithms fior
Madrining," Proceedings of the 1988 American Contrcl Conference, JurE
Noncircular Machining on a Lathe
. Conventionalt r=r(x)
. Noncircular machining: r = r(x,0
Repetitive Control
Discrete Time Formulation & Solution
Repetitive Desired Output: yo@)= yo@-N)
e(k)=yo@)-y(k)
Asymptotic Regulation: l*.-*C)=E
Note: N may be very large: e.g. N - 100, 1000, ....
The repetitive control system is a high order system.,

8. Repetitive Control System
Internal Model of
repetitive signals
Noncircular Machining Movie
(courtesy of Professor T-C. Tsao)
. Early successful example
r Current Research Projects
. Kinematic Kalman Filter (KKfl for End-Effector
Sensing of Robotic Manipulat6rs
. Controller tuning of drive bains with auxiliary
load-side acceleiometers
. Smaft shoes for human walking gait phase
detection.
. Flo<ible joint actuator for human rehabilitation
. Concluding Remarks $
Kinematic Kalman Filter (KKF):Basic ldea
. Poslllon
r Veloclty
r Rotatlon matrlx
4fu e*t.nded Kalman Filter Theory
Tfiir"r"u.
"qr.ti-
is nonlinear.
. The inertia sensors at fast rate and the vision sensor
at dow rate,
. Vision image is obtained after one vision sampling
period delay because of processing of innges.
r Use Extended Kalman Filter Theory to obtain KKF
. Predictor equation based on nonlinear kinematic
eq. Use inertia sensors during prediction.
. Propagation of estimation enor covariance during
prediction by the linearized model.
. Conector equation. Conection when a new image
is obtained from the vision sensor, $
Motivations
r*.,rr.r"rt ;i iobot end-effeaor
lndustdalrcbotseVonmotorercodesonv * rFionsens
End-effector Dosition and motbn are not ecuatdv
klw bmdse ot kinematlc errcr md lrint '
fexibility
. Limitations of vision sensor
' lmage pocssing lime, communication time, etc.
. Slow sampling Ele and latency

9. Experimental Setup
. Kinematic model
' Output measurement: uision sensor
u-P'=P'+rr,
. Early successful example
r Current Research Projects
. Kinematic Kalman Fllter (KKF) for End-Effector
Sensing of Robotic Manipulatbrs
. Controller tuninq of drive trains with auxiliaru
load-side accele-rometers
. Smart shoes for human walking gait phase
detection.
. Flexible joint actuator for human rehabilitation
. Concluding Remarks 22
, lnertial Sensors and Vision Sensor
I
kffiffi;I- r Imptementation of vision sensor
. CalrEra: Phfunfc,c.us AG (Camenlink)
71, = lrl mw
- - irii il,:jo
,- '- l nrstc
. Image processing to measure the
absolute position.
Experimental Results
KKF synchronized with motion controller (7! : I rns)
' PD contoller at eadl Jdnt to bllow straight line parallel b y axis
. KKF routhe b ernbed@ ln motion ccltol bop ( T" - It) 6 )
. lGlnlan filtsr gain calcuhed a priori r* transported as a FF slgnal
Encoder values used to validate
(163,840 ppr )
aG0q6ar
=mI'H
actuator (electfk mobr) and the link
are cmnected via gears fur s@
rcduction and toque ampliftcauon.
r The harmonic Aear is one of the mo6t popular,mear6 br thb purpce;
it is compact and may rcdtre tfle speed at the older of 100 in one
stage.
. The harmonic aear also introduces F
compliance and ftktion b the drire train,
.,1
d Controller Tuning of Drive Trains
=----
' . The servo cortrollers of drive tsairs are mobr Mback
controllers in mo6t industrial appliiations.
' On tfe otfnr lnrd, the drive train qFtem performane is
ewluted by tfie loa+side variables.
. It is often required b Une the mobr feedback conboller
for paficular applicatons b ensure bad-side performance,
. It is desircd to have a field trnirg procedure.
i. In*all additional loa+side
',
I senon for tfre controlh
I tuninq.
i. Aaelercmeers. especially
I MEMSones, are small and
i. easy to insbll, i

10. Iterative Controller Tuning
--------- !/o Load-sidc acm,lcration
Um Nlotor position
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Gradient Estimate
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. Early suacessful example
. Current Research Projects
. Kinematic Kalman Filter (KKF) for End-Effector
Sensing of Robotic Manipulatbrs
. Controller tuninq of drive trains with auxiliarv
load-side acceleiometers
. Smaft shoes for human walking gait phase
detection.
. Flexible joint actuator for human rehabilitation
' Concluding Remark 30
n: controller galns to be tuned.
Human Gait Phases
a CondroG br |@rizire qait phas
a: Hlghs $d ltrshoH O: Lower than Ttrgfbld O: ilot Apptied
*
Plant:
ft a single-joint robol with a resonant mode
around 18 Hz.
Controller Structure:
G Proporlional conlrol is used in the position
loop.
$ Observer slate feedback control with
integral action is used in lhe velocity loop.

11. GCF Measurement System by Air
Bladder
. This s€nsing method has be$er durability and practicality than the FSR sensors or
the force plate.
. Weight ofeadr sensor < 209
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Gait Analysis of Parkinson Disease, Spinal
is Patient using Smart Shoes
i <ii ifie ientral nervous system that
often impairs the suffere/s motor skills, speech, and other
functions
. Symptoms
. Motor symptoms
Tremor (,5.6i): normally 4-6 Hz
Rigidity (ffiESE): stifness, increased muscle tone
Akinesia/ Bndykinesia (*9t. FSI): respectively, slorness or
absence of movement
Posture insrability (44iRffi F$f H g)
, Gait and posture disturbances
. Speech and swallowing disturbance
. Other motor symptoms: fatigue etc. 33
System Configuration
vb6l hedback by showlrE
dE gowd @ntact fo@ to a
padet
u5E{009
I Analog hputs br Murtng
go6d @nbct to@ (4 AI for @h
foot)
Parkinson Disease Patient 1
(Steve D.)
. Disorder description: Parkinson disease
(both side, right side worse) from 5 years
490
. Main problem in gait: skating, sliding
. Physical therapy: for lwo monlhs
. Experimental result with Smart Shoes:
relatively low heel GCF, rehlively high
hallux GCF because of sliding and skating
' Early successful example
r Current Research Projects
' Kinematic Kalman Filter (KKD for End-Effector
Sensing of Robotic Manipulatbrs
. Controller tuninq of drive trains with auxiliarv
load-side acceleiometers
. Smaft shoes for human walking gait phase
detection.
' Flexible joint actuator for human rehabilitation
. Concluding Remarks 36
Flexible Joint Actuator for Human-
K Kong and M, Tornizuka, "Flo{ble Jdnt Acbator for Pauenfs
Proceedings of the IEEE lntemational Symposium on Robot and Human Interadive
37
Cornmunlcation (RO-MAN2007), August 2GA, 2007, W, 1179-1184.
10

12. Design of Flexible Joint Actuator
Encoder 1
DC Motor
G€r Etio = 113:l
Power = 200W
EFcierEy < 70%
K bno l. &6 ad tr.Idhb,'hd d Fdde bhtl@.hld€ltoc keAMoni &h& b /Fg&f
La@MuWtooi'M.
Extreme Case 1: Foot is not touching
Experimental Results
(Flexible Joint Actuator)
n
m
Flexible Jdnt Actuator
Human Subject
Extreme Case 2
Foot is touching ground
- Pz(s)=--;1- e Y(s)
Ius'+Cus+k
wnere 6(5) = |
. Since Pr(s) has the minimum order inY(r)
l,(s) is set as the nominal model.
Feedback Controller Design
Fdezeo map from rk b,
. E"r"is obtalned by Hooke's law.
. In the case of a nonlinear spring , r = /(rard)
the invece tunction is applied.
r Theaim of the contloller is
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regardless of rariauons of the system model.
. The systffi cn be stabilized by a simple
PD contsollef. {r
, Robust Feedback Controller
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13. r Early successful examples
r Current Research Projects
. Kinematic Kalman Filter (KKD for End-Effector
Sensing of Robotic Manipulatbrs
. Controller tuninq of drive trains with auxiliarv
load-side acceleiometers
. Smaft shoes for human walking gait phase
detection.
. Flexible joint actuator for human rehabilitation
. Concluding Remark q
JtFt#€'Hfit;'31tLf:"
Concluding Remarks
presented cases of synergisuc integration of
mechanical hardware, decision making function, and
sensoryactuatorc.
. Control theory often plays a critical role in the synthesis of
mechatronic systems, but it is a part of the syneryy
achieved by modeling, sensi ng/actuation technologies and
decision-making fu nctions,
. For effective synergistic integration, it is important:
. to understand and formulate the problem right,
. t0 select suitable sensing/actuation technologies
r to choose appropriate control theory.
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IEEE Tnns. Evol. Conpul
July' 1997
,.{Trf rEta+t 6L y=
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F=>7t)=7@E6tnt:-#;+
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16

17. ,ftrrJftE:t-€HfE4rE=*F@EqE-+ lc
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18. FIl+a',HtHfi{+{'t m
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19. EfiF+fiER (FtEIY,+i )v) N
InsT = E'I rp(I N * /nX/ry +/6)
3*EEFOtrffi++{'T IR
0
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{rlAl
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FI
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2*-3*
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20

21. EE:rEffiE+ruft|lffiITftH
E+ffit? t?Ffi r?rfrfrf+
#fr+v tJrtfi Ei*
Haruo Kobayashi
FHt+ r+Et3 -#ffir?t++t
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e-mail: k_haruo@el.gunma-u,ac.jp
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24

24. E+;qtjft{fttl}trfjEl:&6
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a |^*tjEffi (DAAf&l*. fit'1€7 >J) 0)
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i ou", L oact ..L=--
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E
Fpql
T lr,r/r TstDl I
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dt F;E;10+s=3tl
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25

25. ADC E E {irE+i{,t'tO
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t---._-
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mbidi"srucuqil
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rsri ,1.i , i*:l+:+
;'il.!lifltj ifiii,1*tF + :$ **:i::.;fii*- :tr.;. l
6r tL ;i ii. f) r',:.* i.-:i";'1 iiiiiii.jlrirl'tir-j'ntit,*
O Apc'co) -#,fl| ftI ffil . lE€r&Ef,iffi I : f 6 F!= ttf,€ {b
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a
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a r*Hft{ffi#ts
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t/ cvosto)FHl:ffitr
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i ) I tv a$#.?ffi E l*ffii r : J rJ
o H{=ffitt.rb
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wk bhE^in {D/n o c o +# Fx tw *
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E= E - E =fza.|
26

26. 2#t*,R7 )vj.t)^,L
=2
t V-?;F+rlEF+oS,{t
vin=23.5 E fFFI :7ttrrJ^h n.,ALe
17+'y1 tr'CEF*riELf:&E
' Vref(1)=16
vref(2)=8
vret(3)=12
Vref(A)=14
Vrel(s)=15
2ft+TA7 )rit)7it^
Do ut=24 +d ( 1 ) 23 + d (222 + d (321 +d (4) + 6 15; 6. 5-9. 5
tF2#7 )v) |
) A L :5CvFAfilf,Et6,(iyJac*E "
lit *. a )F2#IR#7 t ri |
) A t"
Dout=24+d(1 )y4+d(2)y3+d(3)y2+d(4)vl +d(5)+d(6)0.5 -0.5
1<Y<2 l
7,r='17 alrt1:=.61t:itr65" y = 2Z
tE2Ef*#7 tvi |
) Al" O - fi* {t,
Dout=24+d(1 )p(2)+d(2)p(3)+d(3)p(4)+d(4)p(5)'d(5)p(6)*d(6)0.il.5
p(k)AHHI:iA$6. p(k):Affio)E*
26
)Fz€+RRz )t,i U lLa
i,, /I,;FE?ffiTEIHIE
xhsDLS
ztEWR
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/ rr
Dout=4+2-l+0.5-0.5=5
Doul = 4+1+1+
+{€H, @ydli^+.y7eT+t
,,,' =oliit)*;i-os=
1 lty7E -C+Jt#rf L(+rffi iE'c*
tF2Ef**7 )vj.t)AL
sE'vF'l}fi4HE(s2vd.)v)
677v)(k=t,...,6)Al&e
p(2)=7
p(3)=4
glal=2
P{5)=1
p(6)-1
,)ffi0)Etl:*td
f#i+f6"
2tr =1 +p(2)+p(3)+p(4)+p(5)+p(6)
2a =1+7+4+2+1+1=16
M
znu =l+Zp(i)
tffi:L(tr6
affiF,B.ftE-ffiA
n*l-tDAAfFEFa)*EF*F"l
dl
o
J
o
o
o
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o
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-
Long
t-} Lasrstep
, .- First step
)t2€*X#7 )vit)^al:&.5
AD4f4 Htr.fb (JHIEF,IEJ)
Exact DAC settling
- Long
time A,/D conversion time
iil
Correct incomplete settling enor.
lncomplete DAC settling * Short
27

27. ADA+ftxC-l-:'otb+i
Conversion time of each algorithm (14-bit)
1-d _
'zu-
_80
,E
o
E
'a 40
O
o
0
lr
Binary Conventional Proposed
algorithm non-binary non-binary
algorithm algorithm 32
w*.bLw.ADC^0) Hfr'r+
O €fr6o:E'=t
o tr##r,a<ftH
o frffficMos.HroEjT4if#
a ff,87 )vl'J XA ({=+ro}Sfr f,ii)
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H !i. r. i i ; ri; i::: l'.] I :- i i i-: .r!i
j
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-,." il:^ ji-1
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a *&&)
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o ADc|E{tffit?a&ftnft
H e+tE (H*Ftr.tb)
ry E+rFtftlJtfli+*ffii
ERE+ffiE (HE.Ib)
@ {=€l&rE+il,li
2B

28. EE=EIffiEHtrEiEilEE F5ilE
Fx1R?llftt? I+Ell
#fr+V. 7K+ ,H
Kiyoshi Mizui
r9B5+
19A7 +
1990+
1990+
1994+
2003 +
I ee4 +trEf ,tE+FiE,f=+^?tlttrFrHqH
2 o o 5 +- 2 o o 6 + €+lE+FiE,f=+^ ITS (lE=trriE t Z 7 A ) 6ff 4SF 54E^4FF
ITS ffi fr?:/ Z 7 A. ++ l.++F=1iE,f=0)trff fr l.{ff +"
E= I ) -v, )U|EFat
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29

29. tr+Elffiu
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EL)Wffi,
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firltt&L)ffi*Vffil?afd'a#-.
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