designing control systems - mit opencourseware...designing a control system today’s goal:...
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6.01: Introduction to EECS I
Designing Control Systems
March 8, 2011
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Midterm Examination #1
Time: Tonight, March 8, 7:30 pm to 9:30 pm
Location: Walker Memorial (if last name starts with A-M)
10-250 (if last name starts with N-Z)
Coverage: Everything up to and including Design Lab 5.
You may refer to any printed materials that you bring to exam.
You may use a calculator.
You may not use a computer, phone, or music player.
No software lab this week.
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Signals and Systems
Multiple representations of systems, each with particular strengths.
Difference equations are mathematically compact.
y[n] = x[n] + p0 y[n − 1]
Block diagrams illustrate signal flow paths from input to output.
Operators use polynomials to represent signal flow compactly.
Delay
+
p0
X Y
Y = X + p0RY
System Functionals represent systems as operators.
Y 1 Y = HX ; H = =
X 1− p0R
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Feedback, Cyclic Signal Paths, and Poles
The structure of feedback produces characteristic behaviors.
Feedback produces cyclic signal flow paths.
Delay
+X Y
Cyclic signal flow paths → persistent responses to transient inputs.
Delay
+
p0
X Y
We can characterize persistent responses (called modes) with poles.
−1 0 1 2 3 4n
y[n] = pno ; n ≥ 0
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Designing a Control System
Today’s goal: optimizing the design of a control system.
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Example: wallFinder System
Using feedback to control position (lab 4) can lead to bad behaviors.
di[n] = desiredFrontdo[n] = distanceFront
t
do
k = −0.5 t
do
k = −1
t
do
k = −2 t
do
k = −8
What causes these different types of responses ?
Is there a systematic way to optimize the gain k ?
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( )
( )
Analysis of wallFinder System: Review
Response of system is concisely represented with difference equation.
proportional controller: v[n] = ke[n] = k di[n]− ds[n]
locomotion: do[n] = do[n − 1]− Tv[n − 1]
sensor with no delay: ds[n] = do[n]
di[n] = desiredFrontdo[n] = distanceFront
The difference equations provide a concise description of behavior.
do[n] = do[n − 1]− Tv[n − 1] = do[n − 1]− Tk di[n − 1]− do[n − 1]
However it provides little insight into how to choose the gain k.
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Analysis of wallFinder System: Block Diagram
A block diagram for this system reveals two feedback paths.
di[n] = desiredFrontdo[n] = distanceFront
proportional controller: v[n] = ke[n] = k ( di[n]− ds[n]
) locomotion: do[n] = do[n − 1]− T v[n − 1]
sensor with no delay: ds[n] = do[n]
+ k −T + RDi Do−
V
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Analysis of wallFinder System: System Functions
Simplify block diagram with R operator and system functions.
Start with accumulator.
+ k −T + RDi Do−
What is the input/output relation for an accumulator?
+ RX YW
Y = RW = R(X + Y )
Y = R X 1−R
This is an example of a recurring pattern: Black’s equation.
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Check Yourself
Determine the system function H = Y X
.
+ F
G
X Y
1. F
1− F G 2.
F 1 + F G
3. F + 1 1− G
4. F × 1
1− G
5. none of the above
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+ F
G
X YW
Black’s Equation
Y Determine the system function H = .
X
Y = FW = F (X +GY ) = FX + F GY
Y F X ≡ H =
1− FG
forward gain F closed-loop gain H =
1− loop gain FG
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Check Yourself
Determine the system function H = Y X
. 1
+ F
G
X YW
1. F
1− F G 2.
F 1 + F G
3. F + 1 1− G
4. F × 1
1− G
5. none of the above
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Black’s Equation
Black’s equation has two common forms.
+ F
G
X YW
+ F
G
X YW
−
H = F H = F
1 + F G 1− FG
Difference is equivalent to changing sign of G.
Right form is useful in most control applications where the goal is
to make Y converge to X.
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Analyzing wallFinder: System Functions
Simplify block diagram with R operator and system functions.
+ k −T + RDi Do−
Replace accumulator with equivalent block diagram.
+ k −T R1−R
Di Do−
Now apply Black’s equation a second time:
−kT RDo −kT R −kT R= 1−R = =
1− (1 + kT )RDi 1 + −kT R 1−R− kT R1−R
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Analyzing wallFinder: System Functions
We can represent the entire system with a single system function.
+ k −T + RDi Do−
Replace accumulator with equivalent block diagram.
+ k −T R1−R
Di Do−
Equivalent system with a single block:
−kTR1− (1 + kT )RDi Do
Modular! But we still need a way to choose k.
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Analyzing wallFinder: Poles
The system function contains a single pole at z = 1 + kT .
Do −kT R= Di 1− (1 + kT )R
The numerator is just a gain and a delay.
The whole system is equivalent to the following:
R 1−p0 +
p0 R
Di Do
where po = 1 + kT . Here is the unit-sample response for kT = −0.2:
0n
h[n]
0.2
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Analyzing wallFinder
We are often interested in the step response of a control system.
Start the output do[n] at zero while the input is held constant at one.
di[n] = desiredFrontdo[n] = distanceFront
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Step Response
Calculating the unit-step response.
Unit-step response s[n] is response of H to the unit-step signal u[n],which is constructed by accumulation of the unit-sample signal δ[n].
+
R
Hδ[n] u[n] s[n]
Commute and relabel signals.
+
R
Hδ[n] h[n] s[n]
The unit-step response s[n] is equal to the accumulated responses
to the unit-sample response h[n].
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Analyzing wallFinder
The step response of the wallFinder system is slow because the
unit-sample response is slow.
0n
h[n]
0.2
1
0n
s[n]
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Analyzing wallFinder
The step response is faster if kT = −0.8 (i.e., p0 = 0.2).
0.8
0n
h[n]
0n
s[n]
1
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Analyzing wallFinder: Poles
The poles of the system function provide insight for choosing k.
Do −kT R (1− po)R Di
=1− (1 + kT )R
=1− poR
; p0 = 1 + kT
1 Re z
Im z
0 < p0 < 1−1 < kT < 0
monotonicconverging
1 Re z
Im z
−1 < p0 < 0−2 < kT < −1
alternating
converging
1 Re z
Im z
p0 < −1kT < −2
alternating
diverging
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Check Yourself
Find kT for fastest convergence of unit-sample response.
Do Di
= −kT R1− (1 + kT )R
1. kT = −2 2. kT = −1 3. kT = 0 4. kT = 1 5. kT = 2 0. none of the above
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Check Yourself
Find kT for fastest convergence of unit-sample response.
Do −kT R= Di 1− (1 + kT )R
If kT = −1 then the pole is at z = 0.
Do = −kT R Di 1− (1 + kT )R
= R
unit-sample response has a single non-zero output sample, at n = 1.
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Check Yourself
Find kT for fastest convergence of unit-sample response. 2
Do Di
= −kT R1− (1 + kT )R
1. kT = −2 2. kT = −1 3. kT = 0 4. kT = 1 5. kT = 2 0. none of the above
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( ) ( )
Analyzing wallFinder
The optimum gain k moves robot to desired position in one step.
kT = −1
di[n] = desiredFront=1m
do[n] = distanceFront=2m
1 1k = − = −
1/10 = −10
T
v[n] = k di[n]− do[n] = −10 1− 2 = 10 m/s
exactly the right speed to get there in one step!
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Analyzing wallFinder: Space-Time Diagram
The optimum gain k moves robot to desired position in one step.
di[n] = desiredFrontdo[n] = distanceFront
position
time
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Analyzing wallFinder: Space-Time Diagram
The optimum gain k moves robot to desired position in one step.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10
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Analyzing wallFinder: Space-Time Diagram
The optimum gain k moves robot to desired position in one step.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10
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Analyzing wallFinder: Space-Time Diagram
The optimum gain k moves robot to desired position in one step.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10v = 0
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Analyzing wallFinder: Space-Time Diagram
The optimum gain k moves robot to desired position in one step.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10v = 0
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Analyzing wallFinder: Space-Time Diagram
The optimum gain k moves robot to desired position in one step.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10v = 0v = 0
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Analyzing wallFinder: Space-Time Diagram
The optimum gain k moves robot to desired position in one step.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10v = 0v = 0v = 0v = 0v = 0v = 0
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( )
Analysis of wallFinder System: Adding Sensory Delay
Adding delay tends to destabilize control systems.
proportional controller: v[n] = ke[n] = k di[n]− ds[n]
locomotion: do[n] = do[n − 1]− Tv[n − 1]
sensor with delay: ds[n] = do[n − 1]
di[n] = desiredFrontdo[n] = distanceFront
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Analysis of wallFinder System: Adding Sensory Delay
Adding delay tends to destabilize control systems.
di[n] = desiredFrontdo[n] = distanceFront
position
time
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Analysis of wallFinder System: Adding Sensory Delay
Adding delay tends to destabilize control systems.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10
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Analysis of wallFinder System: Adding Sensory Delay
Adding delay tends to destabilize control systems.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10
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Analysis of wallFinder System: Adding Sensory Delay
Adding delay tends to destabilize control systems.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10v = 0
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Analysis of wallFinder System: Adding Sensory Delay
Adding delay tends to destabilize control systems.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10v = 0
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Analysis of wallFinder System: Adding Sensory Delay
Adding delay tends to destabilize control systems.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10v = 0v = −10
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Analysis of wallFinder System: Adding Sensory Delay
Adding delay tends to destabilize control systems.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10v = 0v = −10
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Analysis of wallFinder System: Adding Sensory Delay
Adding delay tends to destabilize control systems.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10v = 0v = −10v = −10
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Analysis of wallFinder System: Adding Sensory Delay
Adding delay tends to destabilize control systems.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10v = 0v = −10v = −10
![Page 43: Designing Control Systems - MIT OpenCourseWare...Designing a Control System Today’s goal: optimizing the design of a control system. Example: wallFinder System Using feedback to](https://reader030.vdocuments.us/reader030/viewer/2022040907/5e7ce86128813b548d26143b/html5/thumbnails/43.jpg)
Analysis of wallFinder System: Adding Sensory Delay
Adding delay tends to destabilize control systems.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10v = 0v = −10v = −10v = 0
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Analysis of wallFinder System: Adding Sensory Delay
Adding delay tends to destabilize control systems.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10v = 0v = −10v = −10v = 0
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Analysis of wallFinder System: Block Diagram
Incorporating sensor delay in block diagram.
di[n] = desiredFrontdo[n] = distanceFront
proportional controller: v[n] = ke[n] = k ( di[n]− ds[n]
) locomotion: do[n] = do[n − 1]− T v[n − 1]
sensor with delay: ds[n] = do[n − 1]
+ k −T + R
R
Di Do−
V
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+ k −T + R
R
Di Do−
V
Analyzing wallFinder: System Functions
We can represent the entire system with a single system function.
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Check Yourself
+ k −T + R
R
Di Do−
V
Find the system function H = Do Di
.
1. kT R1− R
2. −kT R
1 +R − kT R2
3. kT R1− R
− kT R 4. −kT R
1− R − kT R2
5. none of the above
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Check Yourself
Find the system function H = Do Di
.
+ k −T + R
R
Di Do−
V
Replace accumulator with equivalent block diagram.
+ k −T R1−R
R
Di Do−
−kT RDo = 1−R
2 = −kT R 2Di 1 + −kT R
1−R− kT R
1−R
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Check Yourself
+ k −T + R
R
Di Do−
V
Find the system function H = Do Di
. 4
1. kT R1− R
2. −kT R
1 +R − kT R2
3. kT R1− R
− kT R 4. −kT R
1− R − kT R2
5. none of the above
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√
Analyzing wallFinder: Poles
1 Substitute for R in the system functional to find the poles.
z
Do −kT R −kT 1 −kT z = = z = Di 1−R− kT R2 1− z
1 − kT z1 2 z2 − z − kT
The poles are then the roots of the denominator.
1 (
1)2
z = + kT 2±
2
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Poles
Poles can be identified by expanding the system functional in partial
fractions.
Y b0 + b1R + b2R2 + b3R3 += · · · X 1 + a1R + a2R2 + a3R3 + · · ·
Factor denominator:
Y b0 + b1R + b2R2 + b3R3 += · · · X (1− p0R)(1 − p1R)(1 − p2R)(1 − p3R) · · ·
Partial fractions:
Y e0 e1 e2 X
=1− p0R
+1− p1R
+1− p2R
+ · · · + f0 + f1R + f2R2 + · · ·
The poles are pi for 0 ≤ i < n where n is the order of the denominator.
One geometric mode pin arises from each factor of the denominator.
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√( ( )Feedback and Control: Poles
If kT is small, the poles are at z ≈ −kT and z ≈ 1 + kT .
z =)2+ kT = 1
2 1± √
1 + 4kT ≈ (1± (1 + 2kT )) = 1 + kT, −kT 12
1 Re z
Im zz-planekT ≈ 0
Pole near 0 generates fast response.
Pole near 1 generates slow response.
Slow mode (pole near 1) dominates the response.
12
12±
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Feedback and Control: Poles
As kT becomes more negative, the poles move toward each other
and collide at z = .12 when kT = −14
z = 12 ± √(1
2 )2 + kT = 1
2 ± √(1
2 )2 − 1
4 =12 ,
12
21 Re z
Im zz-plane
kT = −14
Persistent responses decay. The system is stable.
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√ √
Feedback and Control: Poles
If kT < −1/4, the poles are complex.
z = 12 ±
(12 )2 + kT = 1
2 ± j −kT − (1
2 )2
1 Re z
Im zz-planekT = −1
Complex poles → oscillations.
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Same oscillation we saw earlier!
Adding delay tends to destabilize control systems.
di[n] = desiredFrontdo[n] = distanceFront
position
time
v = 10v = 0v = −10v = −10v = 0
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Check Yourself
1 Re z
Im zz-planekT = −1
What is the period of the oscillation?
1. 1 2. 2 3. 3
4. 4 5. 6 0. none of above
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Check Yourself
1 p0 = 2
± j
1 Re z
Im zz-planekT = −1
√3
2 = e±jπ/3
n = e±jπn/3 p0
e±j0π/3 , e±jπ/3 , e±j2π/3 , e±j3π/3 , e±j4π/3 , e±j5π/3 , e±j6π/3 ︸ ︷︷ ︸ ︸ ︷︷ ︸ 1 e±j2π=1
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Check Yourself
1 Re z
Im zz-planekT = −1
What is the period of the oscillation? 5
1. 1 2. 2 3. 3
4. 4 5. 6 0. none of above
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Feedback and Control: Poles
The closed-loop poles depend on the gain.
1 Re z
Im zz-plane
If kT : 0→ −∞: then z1, z2 : 0, 1→ 1 2 ,
1 2 → 1
2 ± j∞
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Check Yourself
1 Re z
Im zz-plane
closed-loop poles
12±
√(12
)2+ kT
Find kT for fastest response.
1. 0 2. −14 3. −1
2 4. −1 5. −∞ 0. none of above
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Check Yourself
z = 1 2±
√(1 2
)2 + kT
1 The dominant pole always has a magnitude that is ≥
2.
1 It is smallest when there is a double pole at z =
2.
1 Therefore, kT = −
4.
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Check Yourself
1 Re z
Im zz-plane
closed-loop poles
12±
√(12
)2+ kT
Find kT for fastest response. 2
1. 0 2. −14 3. −1
2 4. −1 5. −∞ 0. none of above
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1 Re z
Im z
1 Re z
Im z
Destabilizing Effect of Delay
Adding delay in the feedback loop makes it more difficult to stabilize.
Ideal sensor: ds[n] = do[n]
More realistic sensor (with delay): ds[n] = do[n − 1]
Fastest response without delay: single pole at z = 0. 1
Fastest response with delay: double pole at z =2. much slower!
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Destabilizing Effect of Delay
Adding more delay in the feedback loop is even worse.
More realistic sensor (with delay): ds[n] = do[n − 1]
Even more delay: ds[n] = do[n − 2]
1 Re z
Im z
21 Re z
Im z
Fastest response with delay: double pole at z = 2.
1
Fastest response with more delay: double pole at z = 0.682.
even slower →
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Check Yourself
R R R+X Y
How many of the following statements are true?
1. This system has 3 poles.
2. unit-sample response is the sum of 3 geometric sequences.
3. Unit-sample response is y[n] : 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1 . . .
4. Unit-sample response is y[n] : 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1 . . .
5. One of the poles is at z = 1.
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Check Yourself
R R R+X Y
How many of the following statements are true? 4
1. This system has 3 poles.
2. unit-sample response is the sum of 3 geometric sequences.
3. Unit-sample response is y[n] : 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1 . . .
4. Unit-sample response is y[n] : 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1 . . .
5. One of the poles is at z = 1.
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Designing Control Systems: Summary
System Functions provide a convenient summary of information that
is important for designing control systems.
The long-term response of a system is determined by its dominant
pole — i.e., the pole with the largest magnitude.
A system is unstable if the magnitude of its dominant pole is > 1.
A system is stable if the magnitude of its dominant pole is < 1.
Delays tend to decrease the stability of a feedback system.
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6.01SC Introduction to Electrical Engineering and Computer Science Spring 2011
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