suspension control with thoughts on modern control brett shapiro 19 may 2015 19 may 2015 - gwadw-...
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![Page 1: Suspension Control with Thoughts on Modern Control Brett Shapiro 19 May 2015 19 May 2015 - GWADW- G1500626 – v3](https://reader038.vdocument.in/reader038/viewer/2022110209/56649e045503460f94af0977/html5/thumbnails/1.jpg)
Suspension Control with Thoughts on Modern Control
Brett Shapiro19 May 2015
19 May 2015 - GWADW- G1500626 – v3
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Can modern control be useful for the suspension systems?
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Quadruple Suspension (Quad)Main (test)
ChainReaction
Chain
Control• Local – damping at M0, R0• Global – LSC & ASC at all 4Sensors/Actuators• BOSEMs at M0, R0, L1
• AOSEMs at L2
• Optical levers and interf. sigs. at L3• Electrostatic drive (ESD) at L3Documentation• Final design review - T1000286• Controls arrang. – E1000617
Purpose• Input Test Mass (ITM, TCP)• End Test Mass (ETM, ERM)Location• End Test Masses, Input Test Masses
R0, M0
L1
L2
L3
3 19 May 2015 - GWADW- G1500626
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Suspension damping feedback
Damping feedback filter+
sensor noise
[m] [N]
• Used to reduce the Qs of the suspension modes
• Good candidate for modern control
19 May 2015 - GWADW- G1500626
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Suspension cavity control
5
Main (test) Chain
Reaction Chain
Cavity control filters
Drift control
Low frequency control
Medium frequency control
High frequency control
19 May 2015 - GWADW- G1500626
Cavity length
• Controls the length of the cavity
• Probably not a good candidate for modern control
• Maybe otheroptimal methods
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Suspension angular controlMain (test)
ChainReaction
Chain
Alignment control filters
Optical levers or wavefront sensors
• Control the pointing of the test mass
• Might be a good candidate for modern control
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Modern Control Example - Damping
Modal Damping+
sensor noise
[m] [N]
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Modal Damping with State Estimation
T1
Suspension
11G
22G
33G
44G
8
Modal damping filters
modal displacementsof all stages
modal forces
Displacementsof every stage
top massdrive
pendulumeigenvector matrix
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Modal Damping with State Estimation
T1
Pendulum
11G
22G
33G
44G
9
Modal damping filters
modal displacementsof all stages
modal forces
top massdrive
N mode plant
Displacementsof every stage
pendulumeigenvector matrix
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Modal Damping with State Estimation
T1
Pendulum
11G
22G
33G
44G
10
Modal damping filters
modal displacementsof all stages
modal forces
top massdrive
N mode plant
N, 1 mode plants
Displacementsof every stage
pendulumeigenvector matrix
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Modal Damping with State Estimation
T1
Pendulum
11G
22G
33G
44G
11
Modal damping filters
modal displacementsof all stages
modal forces
top massdrive
N mode plant
N, 1 mode plants
top massdisplacement
pendulumeigenvector matrix
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Modal Damping with State Estimation
T1
Pendulum pendulumeigenvector matrix
11G
22G
33G
44G
12
Modal damping filters
estimatedmodal displacementsof all stages
modal forces
top massdisplacement
top massdrive
N mode plant
N, 1 mode plants
Estimator
Estimator uses LQR to optimize the noisy sensor(s) for good estimation
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Modal Damping with State Estimation
T1
Pendulum pendulumeigenvector matrix
11G
22G
33G
44G
13
Modal damping filters
estimatedmodal displacementsof all stages
modal forces
top massdisplacement
top massdrive
N mode plant
N, 1 mode plants
Estimator
Estimator design is independent from damping design
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Modal Damping with State Estimation
T1
Suspension pendulumeigenvector matrix
11G
22G
33G
44G
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Modal damping filters
estimatedmodal displacementsof all stages
modal forces
top massdisplacement
top massdrive
N, 1 mode plants
Estimator
easy to add inother sensors with almost no impact on stability
Oplevs or WFS
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10-6
10-4
10-2
100
102
Ma
gnitu
de (
ab
s)
10-1
100
101
-360
0
360
720
1080
1440
Ph
ase
(d
eg)
Bode Diagram
Frequency (Hz)
Modal Damping Loop Gain – This is Stable!?
15
-180
-180
-180
+180
+180
unity gain
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10-6
10-4
10-2
100
102
Ma
gnitu
de (
ab
s)
10-1
100
101
-360
0
360
720
1080
1440
Ph
ase
(d
eg)
Bode Diagram
Frequency (Hz)
Modal Damping Loop Gain – This is Stable!?
16
-180
-180
-180
+180
+180
unity gain
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Humans (loop shaping) Modern controls
• MIMO systems
• Not subject to human biases and strategies
• Quantitatively ‘optimal’ solutions
• ‘Optimal’ only in the context of the cost function
• Unmodeled dynamics
• ‘human readable’ designs
• Distinguishing good and bad results
• Adjusting to the unexpected
• ‘human readable’ designs
• MIMO systems with many coupled loops
• Have individual biases and strategies
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Conclusion – every job has its tool
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Back Ups
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Concerns I often hear about Modern control, and my responses to those concerns
This is just a first attempt to answer these concerns. I think we should agree on the responses as a group,
citing examples. By doing so we’ll be well on our way to exploiting the best each technique has to offer.
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Concerns often heard about Modern Control (MC)
1. We’re just moving the complexity of the design to the cost function.
2. It is hard to predict how a change in the cost will impact the loop.
3. The cost function does not permit the designer to include as much intuition as classical techniques like loop shaping.
4. We don’t know what optimal really means so we end up just turning the knobs of the cost function to get a result the seems OK.
5. Stability is not guaranteed in practice.
6. Only a few people know how to use it.
7. We’ll lose understanding of our own control systems. Too much complexity is buried in algorithms.
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My responses to those concerns1. “We’re just moving the complexity of the design to the cost function.”
In some cases yes. But there are cases where MC simplifies the problem. Modal damping is an example, which is actually a mix of MC and traditional loop shaping. The MC decouples the system into independent modes, which are then controlled using traditional loop shaping. In other cases, MC might be no more or less complex, yet provide better results. In general, keeping the cost function simple helps a lot.
2. “It is hard to predict how a change in the cost will impact the loop.”
Can be yes. Using simple cost functions makes it easier to predict what will happen. If the cost function needs to be so complicated that it is disconnected from the result, then MC may not be the right tool for the problem.
3. “The cost function does not permit the designer to include as much intuition as classical techniques like loop shaping.”
Adding some frequency dependence helps here. Keeping it simple also helps preserve intuition. If the frequency domain shape needs to very complicated, or frequency weighting is required for more than 2 or 3 loops, MC might not be the best choice (at least on its own).
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My responses to those concerns4. “We don’t know what optimal really means so we end up just turning the
knobs of the cost function to get a result the seems OK.”
As before, keeping it simple helps preserve intuition. Also, sometimes it works well simply to scan through the space of cost function values and pick the ones that work best. Modal damping was implemented in this way, by writing an ‘outer’ cost function that optimized the MC cost function with things we actually care about like DARM noise and Q values. Might it seem silly to have a cost function for a cost function, but it does work very well in this case.
5. “Stability is not guaranteed in practice.”
Stability is an issue with any feedback method (even feedforward sometimes). If you start with a good model in the modern controller, any adjustments needed to ensure stability will be minor.
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My responses to those concerns6. “Only a few people know how to use it.”
I think this and the next points are the most serious concerns. My response for this one is that if we want our systems to work at peak performance, we need to make all the tools available to us. People who work on high performance systems should make the effort to become familiar with MC methods. In most cases MATLAB makes them pretty easy to use. A deep understanding of the (usually difficult) math behind them isn’t necessary for effective use.
7. “We’ll lose understanding of our own control systems. Too much complexity is buried in algorithms.”
This is legitimately something too watch out for. In some cases MC can simplify the control without losing much understanding of what’s going on, as in modal damping. In other cases MC can obscure the physics of what’s going on. In those cases we should use loop shaping methods, unless MC solves some essential problem loop shaping can’t. Thus, the point made earlier in these slides to use the right tool in the right place.
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Summarizing what Modern Control is good and bad at and when it is useful for us
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Summarizing what is modern control good at
• MIMO systems– Will utilize many signals seamlessly and take
advantage of built in couplings• Generating theoretically stable solutions
– Even for unstable systems (Para. Inst. perhaps?)• Very simple to use software, e.g. MATLAB• Not being subject to human biases
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Summarizing what is modern control bad at?
• ‘Optimal’ is only in the context of the cost function (inputs vs outputs). – Loss of human intuition– No nonlinear effects like actuation limits
• Stability requires very good models– Will not handle unmodeled dynamics at all, e.g. bounce
and violin modes• The controller is no more complex than the plant
– Though you can augment the plant with any frequency dependence in the cost functions
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So when is modern control useful?
• When the usual methods aren’t meeting the requirements
and /or
• When MC can simplify a MIMO system
and (in all cases)
• When the cost functions are simple
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Modal Damping Notes
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Modal Damping with State Estimation
xf
mf
T1
CartesianCoordinates
(coupled dynamics)
ModalCoordinates(decoupled dynamics)
Estimator
q̂
Pendulum
•
• = pendulumeigenvector matrix• = Cartesian coordinates• = sensor sig.• = estimated modal coord.• = Cartesian damping forces• = modal damping forces• mode damping filter• mode damping gain
qx
mf
f
q̂
x
thii
thiGi
11G
22G
33G
44G
y
y
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-100-50
050
100150
Bode Diagram
Ma
gnitu
de
(d
B)
1 Hz modal plant: P
10-2
10-1
100
101
102
-360-315-270-225-180-135-90-45
04590
Pha
se (
deg
)
Frequency (Hz)
Modal Feedback Design
22ns
sP
31
2n10 ,,,
-100-50
050
100150
Bode Diagram
Ma
gnitu
de
(d
B)
1 Hz modal plant: PFeedback filter: G
10-2
10-1
100
101
102
-360-315-270-225-180-135-90-45
04590
Pha
se (
deg
)
Frequency (Hz)
-100-50
050
100150
Bode DiagramGM = 9.1865 (2.403 Hz), PM = 74.5731 (1.0464 Hz)
Ma
gnitu
de
(d
B)
1 Hz modal plant: PFeedback filter: GLoop gain: GP
10-2
10-1
100
101
102
-360-315-270-225-180-135-90-45
04590
Pha
se (
deg
)
Frequency (Hz)
2222
22
)20(20)5.2()10sin(5
)20(2
ssss
ssG
nn
approximate damping ratio
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Estimator Design
y
q
qCL
fB
q
qA
q
q
ˆ
ˆ
0ˆ
ˆ
ˆ
ˆmmmm
matrixfeedback Estimator
matrixSensor
model Pendulum ,
m
m
L
C
BA mm
m
J
dtJ
m
mTm
TT
LL
Rzzq
qQqq
)min(arg
~
~~~
0
ionamplificat noisesensor ~
~error estimation ˆ~
q
qLz
qqq
Tmm
RQ ,
Weight cost of estimation error
Weight cost of using noisy sensor
?
32G1200694 - ACC 2012 - 27 June
Linear Quadratic Regulator (LQR) design
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Choosing Q and R: Not Unique
33
n
n
n
n
Tn
Tn
T
q
q
q
m
mqqq
~
~...
~
0
...
00
~~...~1
1
2
21
11
mR
R
R
0
...
0
2
1
R
R is still to be determined
Q
amplitude response impulse velocity modal
mode of mass modal 1
i
i
m
im
m
J
dtJ
m
mTm
TT
LL
Rzzq
qQqq
)min(arg
~
~~~
0
G1200694 - ACC 2012 - 27 June
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Solving the R matrix for MIMO Modal Damping
)min(arg
maxmax)( 22,
R
ii
iisR
JR
NTRJ
seconds 10
DOFfor timesettling 4 Stage,
iT is
Hz 10at DOFfor t requiremen noise 4 Stage
Hz 10at DOFfor noisesensor 4 Stage
i
iN i
DOFs i are: x, y, z, yaw, pitch, roll
•
•
xz
Try a bunch of R matrices and see what works best
Measure ‘best’ with an auxiliary cost
function.
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Modal Estimation Cost
10-10
10-9
10-8
10-7
10-6
10-5
10-410
-4
10-3
10-2
10-1
100
101
102
103
R
Cos
t V
alu
es
x Estimator Cost vs. R
Settling Cost, max(Ts2)
Sensor Noise Cost, max(N2)Total Cost, J
R
8109 R
G1200694 - ACC 2012 - 27 June
(x is the laser axis)
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Optimal Noise Amplification
10-1
100
10110
-22
10-20
10-18
10-16
10-14
10-12
10-10
10-8
10-6Contributions to the Test Mass Displacement along the x DOF with R = 9 10-8
Am
plitu
de m
/ H
z
Frequency (Hz)
Ground disturbanceOSEM sensor noiseStage 4 response to ground disturbanceStage 4 response to sensor noiseTotal Stage 4 response
Noise requirement
simulation
G1200694 - ACC 2012 - 27 June
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Comparison of MD Noise Performance
B Shapiro, Modal control with state estimation for advanced LIGO quadruple suspension, MSc Thesis, 2007
simulation
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Generalized Cost Functions for Cavity Length Control
References: G1400853, G1400567, SWG log 11297
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Monte Carlo Design12 million iterations – about 1 day of computation time in MATLAB
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Cavity Performance (simulation)
RMS error = 5.8e-16 mRequirement < 1e-15 m
DAC RMS voltages:Top = 0.21 VUIM = 0.29 VPUM = 0.99 VTM = 0.06 VDAC saturates at 10 V
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