Advanced Motion Control -...
Transcript of Advanced Motion Control -...
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/department of mechanical engineering
Advanced Motion Control
Maarten SteinbuchControl Systems Technology
Department of Mechanical EngineeringEindhoven University of Technology
APC workshop, Vancouver, may 2005
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Contents• background, motion systems• control for dummies• advanced motion control challenges• embedded dynamical systems
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/department of mechanical engineering
Eindhoven University of Technology
• 9 scientific departments, 10 academic Bachelor programmes, 19 Master programmes, 3000 employees, 220 professors, 6800 students, 200 postgraduate students, 450 PhD students• located in the Eindhoven-Aachen-Leuven triangle• ‘mechatronics’ high tech industry:• Philips, ASML, FEI, Assembleon
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/department of mechanical engineering
Mechanical Engineering Department
• 9 full prof., 60 senior research staff, • 18 Post Docs, 105 PhD students, • 700 BSc and MSc students
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Structure of Mechanical Engineering
Thermo Fluids EngineeringComputational and Experimental MechanicsDynamical Systems Design
2 ‘theme Mastertracks’:• Automotive Engineering Science• Micro and Nano Technology
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/department of mechanical engineering
Mechanical Engineering Department
DynamicalSystemsDesign(DSD)
Thermo FluidsEngineering
(TFE)
Computational & Experimental
Mechanics(CEM)
Automotive Engineering Science (2001)
(Sub)-Micron Technology (2003)
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TU/e - W : Full Chairs in DSD Division
• Dynamics and Control:Prof.Dr. Henk Nijmeijer
• Control Systems Technology:Prof.Dr.Ir. Maarten Steinbuch
• Systems Engineering :Prof.Dr.Ir. Koos Rooda
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/department of mechanical engineering
from Industry …
… to Academia
(1999)
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/department of mechanical engineering
TU Eindhoven
Philips
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Isles of AcademiaIsles of Academia
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…
in theory there is no difference between theory and practice
in practice there is
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…2
simulation is like masturbation:the more you do it the more you think it is the real thing!
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bridging the gap
• education: merge classic & modern • bring in real industrial systems• confront PhDs with other disciplines• learn from experimental experience
how to proceed with theory
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Control Systems Technology
• 1 full prof• 2 part-time prof• 7 associate and assistant prof• 4 technical staff members• 20 PhD students• 40 MSc students/year
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• Motion Systems– industrial applications (pick-and-place,
(bio)-robots)– consumer applications (storage systems)– hydraulic servo systems
• Automotive– power trains (in particular CVT)– (passive) car safety systems– vehicle electrical power management
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DaVinci surgery robot © Intuitive Surgery Inc.
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Automotive Safety Restraint Systems
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Zero-Inertia
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Continously Variable Transmission
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1/1,000 mm
Source: intel, ICE
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Stepper Exposure Method
Reticle
Lens
Reticle
Wafer
Design
Source: ASML
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Illumination
Aberrations Scatter
Sourceoptimization
Doseoptimization Mask optimization software
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Mechanical Electronic / Electrical Pneumatic Hydraulic Chemical
AcousticalThermal Optical Software
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Motion Control for
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Motion Systems
m F
x
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x
Servo force ?Mass M
Disturbance F d
Fs
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Mechanical solution:
Mkf
π21
=
xdxkF ⋅−⋅−=
x
Mass M
k
d
F Disturbance Fd
Force spring damper
Eigenfrequency
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x
Mass M
Mk
f p
π21
=
xkxkF vps ⋅−⋅−=
Fservo
damping servo:stiffness servo:
v
p
kk
Eigenfrequency
Servo force
Servo analogon:
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x
Mass M
Disturbance F d
Fs
Example:
Slide: mass = 5 kgRequired accuracy 10 µm at all timesDisturbance (f.e. friction) = 3 N
1. Required servo stiffness?2. Eigenfrequency?
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h or xsx
)()(:damper-Spring
xhdxhkF −+−=
How to move to / follow a setpoint:
)()( :Controller
xxkxxkF svsps −+−=
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dtdkk vp +
controller
Fservo
process
MassxFxs
+
-
e
xsx
Fd
Kp/kv-controller or PD-controller
Fdisturbance
+
+
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?
?
=
=
v
p
k
k
controllerxs
Fservo
processxe+
-
v
p
k
kerror⇒ stability⇔
Trade off
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xsxSetpoints:
What should xs look like as a function of time, when moving the mass?
(first order, second order, third order,….?)
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x
F M
Apply a force F (step profile):
⇓
=
)()( txMtF
x(t) is second order, when F constant
Second order profile requires following information:
- maximum acceleration- maximum velocity- travel distance
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Example
2max
max
sec/36.1500sec/6320
3.62
radeAccradVel
radPos
≈=≈=≈=
πππ
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Concluding remarks time domain tuning
xsx
Mkv
kp
A control system, consisting of only a single mass m and a kp/kv controller (as depicted below), is always stable.kp will act as a spring; kv will act as a damper
As a result of this: when a control system is unstable, it cannot be a pure single mass + kp/kv controller(With positive parameters m, kp and kv)
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Time domain: Monday and Thursday at 22:10
Frequency domain:twice a week
Frequency domain
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-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
time in sec
excitation force in N and displacement in m
excitation force (offset 0.1 &scaling 1e-4)
response
F
x
Mweak spring M = 5 kg
(f = 2.5 Hz)
F t t( ) sin( )= 400 2 7π
H Hz e m NH Hz
( ) . / /( )
7 0 045 400 1 47 1800
≈ = −
∠ ≈ −
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measurement mechanics stage
102 103-80
-60
-40
-20
frequency in Hz
amplitude in dB
102 103-200
0
200phase in deg
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Transfer function:
kdsMssFsxsH
++== 2
1)()()(
1Ms2 + ds + k
F x
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Bode plot of the PD-controller:
100 101 10260
80
100
frequency in Hz
amplitude in dB
100 101 1020
50
100phase in deg
kp = 1500 N/m; kv = 20 Ns/m
+1
+0
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H(s)exs +
- Fs
xC(s)
Fd
++
Four important transfer functions
)()()( sHsCsL =1. Open loop
)()(1)()()()(sHsC
sHsCsxxsTs +
==
S s ex
sC s H ss
( ) ( )( ) ( )
= =+
11
2. Closed loop
3. Sensitivity
4. Proces Sensitivity H s xF
s H sC s H sps
d
( ) ( ) ( )( ) ( )
= =+1
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Mimo
Filters
•Integral action•Differential action•Low-pass•High-pass•Band-pass•Notch filter
PeeDeePeeEye
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Integral action :
X(t) Y(t)siτ
1
τI integral time constant τI =1/ki
-1
ω=2πf0°
-90°
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+90°
0°
ω
+1Differential action
ωε
ωε
kujsuksH ==== ;;
ks uε
ω
ddd
fπωτ
21==
+90°
0°
1+=
sksu
dτε“tamme” differentiator :
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50
TU/e
/department of mechanical engineering
“lead” filter
s
sssuH
d
d
γττ
ττ
ε +
+=
++
==1
111
2
1
γ>1
0°
+90°
ω
11
1τ
ω =2
21τ
ω =
1
522
1 −≈ττγ
2121
1ττ
ωωω ==c
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51
TU/e
/department of mechanical engineering
P+I+D
ω
+90°
0°
-90°
ii τ
ω 1=
dd τ
ω 1=
-1 +1
⎟⎟⎟⎟
⎠
⎞
⎜⎜⎜⎜
⎝
⎛
+
+⎟⎟⎠
⎞⎜⎜⎝
⎛+==
γττ
τε ss
skuH
d
d
i 1
111
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52
TU/e
/department of mechanical engineering
2nd order filter
121
21
2
++==
ωβ
ωε ss
kuH
ε
u(t)- s
1ωs
1ω
β2
-180°1ωω =
β21
0 °
-90 °
-2
1
Top: .1 21 βωω −=o
ε(t)
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53
TU/e
/department of mechanical engineering
General 2nd order filters
12
12
222
2
21
121
2
++
++==
ωβ
ω
ωβ
ωε ss
ssuH
+2
0°
+180°
1
2
1
2⎟⎟⎠
⎞⎜⎜⎝
⎛ωωω1≠ω2General:
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54
TU/e
/department of mechanical engineering
21 ωω ≥
0°
1
+2
-2⎟⎟⎠
⎞⎜⎜⎝
⎛2
1
22
ωω
ω
-180°ω2 ω1
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55
TU/e
/department of mechanical engineering
“Notch”-filter :ω1= ω2
-180°
ampl.
2
1
ββ
fase 0°
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56
TU/e
/department of mechanical engineering
Loop shaping procedure for motion systems1. stabilize the plant:
add lead/lag with zero = bandwidth/3 and pole = bandwidth*3, adjust gain to get stability; or add a pure PD with break point at the bandwidth
2. add low-pass filter:choose poles = bandwidth*6
3. add notch if necessary, or apply any other kind of first or second order filter and shape the loop
4. add integral action:choose zero = bandwidth/5
5. increase bandwidth:increase gain and zero/poles of integral action, lead/lag and other filters
during steps 2-5: check all relevant transfer functions, and relate to disturbance spectrum
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57
TU/e
/department of mechanical engineering
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79
TU/e
/department of mechanical engineering
Implementation issues
1. sampling = delay: linear phase lag
for example: sampling at 4 kHz gives phase lag due to Zero-Order-Hold of:
180º @ 4 kHz18º @ 400 Hz9º @ 200 Hz
2. Delay due to calculations3. Quantization (sensors, digital representation)
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80
TU/e
/department of mechanical engineering
Feedforward design
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81
TU/e
/department of mechanical engineering
Feedforward based on inverse model
sx2ms
1sKK vp +
2ms
x
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82
TU/e
/department of mechanical engineering
Example: m=5 [kg], b=1 [Ns/m],
2nd degree setpoint
0.5
1
xs[m
]
0
0.5
1
1.5
vs[m
s-1]
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6-4
-2
0
2
4
0
t [s]
as
[ms-
2]
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83
TU/e
/department of mechanical engineering
Example: tracking error, no feedforward
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8-2
-1.5
-1
-0.5
0
0.5
1
1.5
2x 10-3
t [s]
erro
r [m
]
viscous damping effect
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84
TU/e
/department of mechanical engineering
Example: tracking error, with feedforward
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8-2
-1.5
-1
-0.5
0
0.5
1
1.5
2x 10-3
t [s]
= 0.9, = 0faKfvK
= 0.9, = 4.5 faKfvK
erro
r [m
]
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85
TU/e
/department of mechanical engineering
feedforward structure
sxH(s)
fvK
x
faK
C(s)
sx
sx
)sxsign(fcK
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86
TU/e
/department of mechanical engineering
3rd degree setpoint trajectory
xs[m
]
0
0.5
1
1.5
vs[m
s-1]
0
0.5
1
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8-4
-2
0
2
4
as
[ms-
2]
t [s]
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87
TU/e
/department of mechanical engineering
Parasitic Dynamics
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88
TU/e
/department of mechanical engineering
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89
TU/e
/department of mechanical engineering
Three Types of Dynamic Effects
- Actuator flexibility
- Guidance flexibility
- Limited mass and stiffness of frame
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90
TU/e
/department of mechanical engineering
1. Actuator flexibility
k
x
d
SensorMotorFs
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91
TU/e
/department of mechanical engineering
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100 101 102-150
-100
-50
frequency in Hz
amplitude in dB
100 101 102-200
0
200phase in deg
x1F
-2
-2
c
d
M1
x1
M2F
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100 101 102-150
-100
-50
frequency in Hz
amplitude in dB
100 10110
2-200
0
200phase in deg
H =xF
2
xF
1
xF
2
c
d
M1
x2
M2F
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94
TU/e
/department of mechanical engineering
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95
TU/e
/department of mechanical engineering
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96
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/department of mechanical engineering
2. Guidance flexibility
k
x
Fs
M, J
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97
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/department of mechanical engineering
3. Limited mass and stiffness of frame
x
FsMotor
Frame
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98
TU/e
/department of mechanical engineering
Motion Control Properties
• experimentation is ‘cheap’Disturbance Design Cycle: 7 min FRF measurement,
model, loopshape, implementation• plant decoupling, i.e. SISO• feedforward: low-order model-based• feedback: loopshaping• key limitation: bode gain/phase - sensitivity integral
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99
TU/e
/department of mechanical engineering
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100
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/department of mechanical engineering
Gunter Stein’s Bode Lecture, CDC 1989
IEEE Control Systems Magazine, 23 (2003), pp 12- 25
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101
TU/e
/department of mechanical engineering
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102
TU/e
/department of mechanical engineering
Motion Control Challenge:
how to cope with Bode sensitivity limitation?
0)(log0
=∫∞
ωω djS
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103
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/department of mechanical engineering
directions of motion control research
• MIMO loopshaping• nonlinear control of linear systems (reset…) • disturbance-based modelling and control• data-driven control
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104
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directions of motion control research
• MIMO loopshaping• nonlinear control of linear systems (reset…) • disturbance-based modelling and control• data-driven control
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Sensitivity dirt
‘Loop’ 1
‘Loop’ 2
… ‘Loop’ n
MIMO integral constraints…
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106
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directions of motion control research
• MIMO loopshaping• nonlinear control of linear systems (reset…)• disturbance-based modelling and control• data-driven control
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107
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Problem formulation
• Do there exist nonlinear feedback controllers that give better ‘performance’ for linear motionsystems than linear solutions?
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Approach
• Performance measures?• Plant is linear, but• disturbances and specifications ‘change’• Use LPV for synthesis?• How about non-smooth (reset) filters
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Bode Gain/Phase relation
Slope = n means
phase = n*90 degrees
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SPAN- filter
rectifier low-pass
X
sign lead
be creative with control!
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directions of motion control research
• MIMO loopshaping• nonlinear control of linear systems (reset…) • disturbance-based modelling and control• data-driven control
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disturbance-based modelling and control
• disc errors vs shocks optical storage • stochastic vs deterministic disturbances• repetitive vs a-periodic setpoints or disturbances
Internal model principle….
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116
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117
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Iterative Learning Control (ILC)
( ) 11 <− PSLQkk ee <+1
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120
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directions of motion control research
• MIMO loopshaping• nonlinear control of linear systems (reset…) • disturbance-based modelling and control• data-driven control
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122
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Machine-in-the-loop design
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Data-driven control• Examples:
– data-based LQG control– iterative feedback tuning– virtual reference feedback tuning– unfalsified control
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Problem statement
• Design a SISO LTI controller C for LTI plant P
r e u yPC+
-
• Control objective: realize the desired So and To
,1
1o
o PCS
+= .
1 o
oo PC
PCT+
=
• Ideal controller Co:
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Data-based controller design
• The controller class: }.)()({)},({ p θβθ zzCzC T=
• Cp(z) is directly prescribed by the designer: notches,
integrators, etc.• Basis functions: .)]()()([)( 10
Tn zβzβzβz …=β
• Tuning parameters: .]θθθ[ 10T
n…=θ
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)()()()( ooo zPzSzCzT =• Constraint on Co:
• Model-based cost function:
( ) 22ooMB )()()(),()()( zWzPzSzCzTJ θθ −=
• Processing the measurements:
)()(),()()()()()(),()()( oooo tyzSzCtuzTtuzPzSzCtuzT θθ =⇒=
• Data-based cost function:
( ) 2
1ooDB ])()(),()()()([1)( ∑ −=
=
N
t
N tyzSzCtuzTzLN
J θθ
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129
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130
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131
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Unfalsified control
Given a set of controllers, implement one, use the I/O data, and check which part of the set is not feasible, then change the set and iterate
Safanov, Tsao 1997
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134
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135
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Explore Motion Control Properties
• experimentation is ‘cheap’Disturbance Design Cycle: 7 min FRF measurement,
model, loopshape, implementation Data based• plant decoupling, i.e. SISO MIMO disturbances• feedforward: low-order model-based Learning control• feedback: loopshaping nonlinear control• key limitation: bode gain/phase - sensitivity integral
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The EndThe End