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Integrated Play-Back, Sensing, and Networked Control Vincenzo Liberatore Division of Computer Science Research supported in part by NSF CCR-0329910, Department.

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Presentation on theme: "Integrated Play-Back, Sensing, and Networked Control Vincenzo Liberatore Division of Computer Science Research supported in part by NSF CCR-0329910, Department."— Presentation transcript:

1 Integrated Play-Back, Sensing, and Networked Control Vincenzo Liberatore Division of Computer Science Research supported in part by NSF CCR , Department of Commerce TOP , NASA NNC04AA12A, and an OhioICE training grant.

2 V. LiberatoreControl Playback2 Networked Control Computing in the physical world Components –Sensors, actuators –Controllers –Networks

3 V. LiberatoreControl Playback3 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

4 V. LiberatoreControl Playback4 Information Flow Flow –Sensor data –Remote controller –Control packets Timely delivery –Stability –Safety –Performance

5 V. LiberatoreControl Playback5 Autonomy S&R and real-time –Autonomy Hide networked RT Hard to build a fully reliable system –Tele-operation Network non-determinism is serious problem S&R –Reduce time constants –Especially important for unexpected occurrences [NLN02] Tele-operationAutonomy S&R

6 V. LiberatoreControl Playback6 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

7 V. LiberatoreControl Playback7 Algorithm

8 V. LiberatoreControl Playback8 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

9 V. LiberatoreControl Playback9 Algorithm Send regular control –Playback time Late playback okay –Expiration Piggyback contingency control

10 V. LiberatoreControl Playback10 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 X X

11 V. LiberatoreControl Playback11 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 ii  i+1

12 V. LiberatoreControl Playback12 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

13 V. LiberatoreControl Playback13 Playback Delays (I) Calculate current RTT variability ifthen Positive variability coefficient Negative variability coefficient Update min RTT estimate Age min RTT estimate Calculate 

14 V. LiberatoreControl Playback14 Playback Delays (II) ifthen else Attempt to avoid quashed packets Increase sampling period

15 V. LiberatoreControl Playback15 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

16 V. LiberatoreControl Playback16 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

17 V. LiberatoreControl Playback17 Evaluation

18 V. LiberatoreControl Playback18 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

19 V. LiberatoreControl Playback19 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

20 V. LiberatoreControl Playback20 Metrics –Root-mean square output –Output: 99-percentile Comparison –Open-loop plant u(t)=0 –Proportional controller (no buffer) –Proportional controller with constant delays

21 V. LiberatoreControl Playback21 Plant output Open LoopPlay-back

22 V. LiberatoreControl Playback22 Packet losses Figure 8

23 V. LiberatoreControl Playback23 Sampling period Imperfection of the control pipe Root-mean-square error

24 V. LiberatoreControl Playback24 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)

25 V. LiberatoreControl Playback25 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


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