Integrated Play-Back, Sensing, and Networked Control Vincenzo Liberatore Division of Computer...

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