integrated play-back, sensing, and networked control vincenzo liberatore division of computer...
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Integrated Play-Back, Sensing, andNetworked Control
Vincenzo LiberatoreDivision of Computer Science
Research supported in part by NSF CCR-0329910, Department of CommerceTOP 39-60-04003, NASA NNC04AA12A, and an OhioICE training grant.
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Networked Control
• Computing in the physical world
• Components– Sensors, actuators– Controllers– Networks
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Networked Control
• Enables– Industrial automation [BL04]– Distributed instrumentation [ACRKNL03]– Unmanned vehicles [LNB03]– Home robotics [NNL02]– Distributed virtual environments [LCCK05]– Power distribution [P05]– Building structure control [SLT05]
• Merge cyber- and physical- worlds– Networked control and tele-epistemology [G01]
• Sensor networks– Not necessarily wireless or energy constrained– One component of sense-actuator networks
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Information Flow
• Flow– Sensor data– Remote controller– Control packets
• Timely delivery– Stability– Safety– Performance
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Playback Buffers [Infocom 2006]
• Play-back buffers– Main objective– Smooths out network non-determinism
• Multimedia buffers– Important source of inspiration– Physics versus multimedia quality– Playback delay computed in advance
• Affects control signal computation– Round-Trip Times
• TCP RTO– Another source of inspiration– Large time-out cost
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Algorithm
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Main Ideas
• Predictable application time– If control applied early, plant is not in the state
for which the control was meant – If control applied for too long, plant no longer
in desired state
• Keep plant simple– Low space requirements
• Integrate Playback, Sampling, and Control
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Algorithm
• Send regular control– Playback time
• Late playback okay
– Expiration
• Piggyback contingency control
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Deadwood packets• Old
– Received after the expiration time• Out-of-order
– Later control more appropriate for current plant state• Would get us into a deadlock
– New packet resets the playback timer– Keep resetting until no signal applied– “Quashed” packet
• Discard!
plant
controller
Playback delay XX
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Countermand control
• Scenario– Packet i+1 overtakes packet I – i+1 << i
– Likely caused by delay spike
• New signal countermands previous one
plant
controller
Playback delay ii+1
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Playback delays• Modular component• Compute playback delay and sampling period T• Use short term peak-hopper [EL04]
– Original peak-hopper for TCP RTO• Too conservative for networked control
– Aggressively attempt to decrease • Aggressively attempt to decrease T• Add upper bound on playback delay
– Avoid dropping deadlock packets– Bound ≤ T+RTT
• Caps and T
• Must estimate lower-bound on RTT– Use symmetric of peak-hopper– Add negative variability estimate to compensate for short-term
memory
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Playback Delays (I)
0
01
r
rr
}1},9375.0,2min{max{ BB
},min{)1(' 01 rrCr
Calculate current RTT variability
':16
'?' minminminmin r
rrrrrr
},max{)1( 01 rrB
0if then
Positive variability coefficient
Negative variability coefficient
Update min RTT estimate
Age min RTT estimate
Calculate
}2/1,4/4/3max{ CC
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Playback Delays (II)
min' rT
minrT
16
' minrTTT
if
min' rT
then
else
Attempt to avoid quashed packets
Increase sampling period
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Control Pipes
• Bandwidth and delays– is playback delay– T is sampling period
• 1/T proportional to bandwidth
• Control pipe– T«– Multiple in-flight packets
• Pipe depth– Bound by constraint ≤ T+RTT– Keep pipe predictable
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Observer
• Estimate future plant state– Plant sample current state, including local variables– Keep log of outstanding control packets
• Assumption on packet delivery– Future packet delivery is uncertain
• Purge from log– Old packets– Packet that should be overtaken by new control
• Countermands signals generated when delay spike is transient
– Out-of-order packets
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Evaluation
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Network Model
• Simulated network• Losses: Gilbert model• Delays
– Shifted Gamma distribution
– Heavy tail
– Low probability of out-of-order delivery
– Correlate delays to introduce delay spikes
• Wide-area implementation• Use RT scheduling whenever possible• Use otherwise unloaded machines
– RT made little difference
• Host worldwide, heterogeneous conditions
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Plant
• Scalar linear plant– Plant state x(t)– Input u(t) (control)– Output y(t)– Disturbances v(t), w(t)
• Akin to white noise
• Deadbeat controller– Aggressive
)()()(
)()()()(
twtxty
tvtbutaxtx
1;
aT
aT
e
e
b
akkyu
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Metrics
• Metrics– Root-mean square output– Output: 99-percentile
• Comparison– Open-loop plant u(t)=0– Proportional controller (no buffer)– Proportional controller with constant delays
22 ym
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Plant output
Open Loop Play-back
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Packet losses
Figure 8
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Sampling period
Imperfection of thecontrol pipe
Root-mean-square error
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Conclusions (I)• Sense-and-Respond
– Merge cyber-world and physical world– Critically depends on physical time
• Playback buffers integrated with – Sampling (adaptive T)– Control (expiration times, performance
metrics)
• Packet losses– Reverts to open loop plant (contingency
control)
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Conclusions (II)
• Playback delay – Adapts to network conditions
• Sampling period T – Avoids imperfection of control pipe
• Simulations and emulations– Low variability around set point– Robust