Integrated%20Play-Back,%20Sensing,%20and%20Networked%20Control

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Integrated Play-Back, Sensing, andNetworked
Control

• Vincenzo Liberatore
• Division of Computer Science

Research supported in part by NSF CCR-0329910,
Department of Commerce TOP 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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Autonomy

• SR and real-time
• Autonomy
• Hide networked RT
• Hard to build a fully reliable system
• Tele-operation
• Network non-determinism is serious problem
• SR
• Reduce time constants
• Especially important for unexpected occurrences

NLN02
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Networked Evaluation EESR 2005
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Metrics

• Disturbance cancellation
• Objective
• The SR system should do what it is supposed to
  do
• In spite of network non-determinism and
  uncertainty in the environment
• Way out
• Use simple tasks
• Scalability L04
• Number of nodes
• Space networks?
• Geographic
• Administrative
• Functional
• Conclusion
• RT SR benchmarks needed!

• Stability (and safety)
• Objective
• Remote controller makes unstable system stable
• Extensive research
• Z01 and references therein
• Problem
• Errors, network partitions, failures make
  stability impossible
• Tracking
• Objective
• The SR system should do what it is supposed to
• In spite of network non-determinism (failures,
  security, etc.)
• Problem
• Benchmarks (NIST?)

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Methodology (I) Co-Simulation
BLP03, HLB05
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A Modest Proposal

• Application benchmark
• National Lambda Rail
• NLR is planned to be capable of supporting both
  production and experimental networks.
• Not a single network or a single test bed but
  facilities to build multiple networks and
  multiple test beds at all of layers 1-3 including
  optical, switched, and routed.
• Goal is to have both persistent and flexible
  infrastructure(s)
• Foster network research
• Support QoS
• Real-Time Overlay
• Support end-to-end RT SR

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Playback Buffers Infocom 2006
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Playback Buffers

• 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!

controller
plant
Playback delay
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Countermand control

• Scenario
• Packet i1 overtakes packet I
• ti1 ltlt ti
• Likely caused by delay spike
• New signal countermands previous one

controller
plant
ti
Playback delay
ti1
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Playback delays

• Modular component
• Compute playback delay t 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 t
• Aggressively attempt to decrease T
• Add upper bound on playback delay t
• Avoid dropping deadlock packets
• Bound t TRTT
• Caps t 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)
Calculate current RTT variability
Positive variability coefficient
Negative variability coefficient
if
then
Update min RTT estimate
Age min RTT estimate
Calculate ?
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Playback Delays (II)
if
then
Attempt to avoid quashed packets
else
Increase sampling period
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Control Pipes

• Bandwidth and delays
• t is playback delay
• T is sampling period
• 1/T proportional to bandwidth
• Control pipe
• Tt
• Multiple in-flight packets
• Pipe depth
• Bound by constraint t TRTT
• 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

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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

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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 the control pipe
Root-mean-square error
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Agent-Oriented SR Software WORDS 2003
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Agent-oriented SR software

• Progress
• Agent-oriented platform
• Compliant control
• Future work
• Application-oriented middleware
• E.g., Scheduling of mobility
• AI (knowledge, planning, learning)
• Security

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Approach Outline

• Local robot control virtual attractors
• Interface for higher-level distributed sw
  components
• Reason about robot behavior
• Encapsulate intelligence needed to
• Cope with
• Long delays
• Imprecise modeling and unstructured environments
• Establish predictable robot behavior and safety
• Distributed control
• Agent-based

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Compliant Control
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What happens if flawed instructions are issued?
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Agent types
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Hierarchical organization
Chain of command
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Virtual Containment

• Analogy
• A robotic platoon contains individual robot
• Not necessarily in terms of ontology
• Application
• Task-oriented teams
• Layering

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Experience
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Acknowledgments

• Students
• Ahmad al-Hammouri
• David Rosas
• Zakaria Al-Qudah
• Huthaifa Al-Omari
• Nathan Wedge
• Qingbo Cai
• Prayas Arora
• Colleagues
• Michael S. Branicky

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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 t
• 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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Conclusions (III)

• Remote supervision of robotic manipulation
• Compliant control
• Local encapsulation
• Gentle, compliant, tolerant to network vagaries
• Agent-based software
• Hierarchical
• Demonstration
• Future work middleware, AI, security

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