Dependable Wireless Control through Cyber-Physical Co-Design

ChenyangLuCyber-PhysicalSystemsLaboratoryDepartmentofComputerScienceandEngineering

WirelessforProcessAutoma1on

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Emerson•  5.9+ billion hours

operating experience

•  26,200+ wireless field networks

$944.92 million by 2020 [Market and Market] Courtesy:EmersonProcessManagement

Offshore Onshore

Killer App of Sensor Networks!

WirelessHART

Industrial wireless standard for process automation

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Ø  Reliability and predictabilityq  Multi-channel TDMA MACq  One transmission per channelq  Redundant routes

Ø Centralized network managerq  Collect topology informationq  Generate routes and scheduleq  Change when devices/links break

TheControlChallenge

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sensordata

Sensor

Actuator

controlcommand

Controller

Dependable control requires•  real-time •  control performance•  resilience to loss

Most of today’s industrial wireless networks are for monitoring.

TowardsDependableWirelessControl

1.  Real-time wireless networks and analysis

2.  Optimizing control performance over wireless

3.  Resilient yet efficient wireless control under data loss.

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This cannot be accomplished by wireless or control design alone à Cyber-Physical Co-design of Wireless and Control

TheReal-TimeProblem

Ø A feedback control loop incurs a flow Fi q  Route: sensor à … à controller à … à actuator

q  Generate packet every period Pi

q  Multiple control loops share a network

Ø  Each flow must meet deadline Di (≤ Pi)q  Stability and predictable control performance

Ø  Research problemsq  Real-time transmission scheduling à meet end-to-end deadlinesq  Fast schedulability analysis à adapt to wireless dynamics through

admission control and rate adaptation

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DelaysinWirelessHART

A transmission is delayed by

Ø  channel contention: all channels are assigned to other transmissions

Ø  transmission conflict over a same nodeq  contributes significantly to latency!

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2 1

•  1 and 5 conflict•  4 and 5 conflict•  3 and 4 do not

4 5 3

FastDelayAnalysis

Ø Compute upper bound of the delay for each flowq  Sufficient condition for real-time guarantees

Ø Channel contention à multiprocessor task schedulingq  A channel à a processorq  Flow Fi à a task with period Pi, deadline Di, execution time Ci q  Leverage existing response time analysis for multiprocessors

Ø Account for delays due to transmission conflicts

Ø  Fast delay analysesq  Fixed priority scheduling [RTAS 2011, TC, RTSS 2015]q  Earliest Deadline First [IWQoS 2014]

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DelayduetoConflict

Ø  Low-priority flow Fl and high-priority flow Fh, conflict à delay Fl

Ø  Q(I,h): #transmissions of Fh sharing nodes with Fl q  In the worst case, Fh can

delay Fl by Q(l,h) slots

Ø  Conflicts contributes significantly to delaysq  Must be considered in

algorithms and analysis!

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Fl delayed by 2 slots

Fl delayed by 2 slots

Fl delayed by 1 slot

Real-TimeWirelessNetworkingØ  WirelessHART stack in TinyOS

q  Implementation on a testbed of 69 TelosB motes.q  Network manager (scheduler + routing).

Ø  Conflict-aware real-time schedulingq  Fixed priority assignment [ECRTS 2011]

q  Conflict-aware Least Laxity First [RTSS 2010]

Ø  Energy-efficient routing [IoTDI 2016]

Ø  Emergency communication [ICCPS 2015]

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WUSTLwirelesssensornetworktestbed

Op1mizeControloverWireless

ObservationØ Wireless resource is scarce and dynamic

Ø Cannot afford separating wireless and control designs

Cyber-Physical Co-DesignØ Holistic co-design of wireless and control

ExamplesØ  Rate selection for wireless control [RTAS 2012, TECS]

Ø  Scheduling-control co-design [ICCPS 2013]

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RateSelec1onforWirelessControl

Ø Optimize the sampling rates of control loops sharing a WirelessHART network.

Ø  Rate selection must balance control and network delay.q  Low sampling rate à poor control performance

q  High sampling rate à long delay à poor control performance

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Ø  Control cost of control loop i under rate fi [Seto RTSS’96] q  Approximated as with sensitivity coefficients

ControlPerformanceIndex

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Ø  Digital implementation of control loop iq  Periodic sampling at rate fiq  Performance deviates from continuous counterpart

€

α i e−β i fi

Ø  Overall control cost of n loops:

€

α i e−β i fi

i=1

n

∑€

α i, βi

delayi ≤1/ fi

TheRateSelec1onProblem

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€

f = { f1, f2,, fn}

€

fimin ≤ f i ≤ f i

max

minimize control cost

€

α i e−β i fi

i=1

n

∑

subject to

Ø  Constrained non-linear optimization

Ø  Determine sampling rates

Delay bound

PolynomialTimeDelayBounds

Ø  In terms of decision variables (rates), the delay bounds are

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q  Non-linear q  Non-convex q  Non-differentiable

Lagrangedualofo

bjecDve

Rateofcont

rolloop6

Controlcost

Cyber-PhysicalCo-Design

Ø  Relax delay bound to simplify control optimizationØ Derive a convex and smooth, but less precise delay bound.

Ø  Rate selection becomes a convex optimization problem.➠ Optimize control performance efficiently at run time!

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A. Saifullah, C. Wu, P. Tiwari, Y. Xu, Y. Fu, C. Lu and Y. Chen, Near Optimal Rate Selection for Wireless Control Systems, ACM Transactions on Embedded Computing Systems, 13(4s), Article 128, April 2014.

ResilientControlunderDataLoss

Ø Data loss causes instability and degrades control performance.

Ø  State of the Artq  Control: control design to tolerate data loss.

q  Wireless: redundancy reduces loss at high resource cost.

Ø Cyber-physical co-designq  Incorporate resilient control design

q  Tailor wireless protocols for control designq  Resilient wireless control at low resource cost

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HandleDataLossfromSensors

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Ø  State Observer estimates system states based on a system model even if there is no new data from sensors

ModelPredictiveControl

ExtendedKalman Filter

Actuators

Sensors

Reference

[ ( ), ( 1), , ( )]u k u k u k w+ +, (, (, (, (

( )y kˆ( )y k

ˆ( )x k

Buffer

Plant

ˆ( )u k

B. Sinopoli et al., Kalman filtering with intermittent observations. IEEE Transactions on Automatic Control, 49(9):1453–1464, 2004.

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HandleDataLossfromController

Ø Model Predictive Controlq  Controller computes control inputs in the next w+1 sampling

periods: u(k), u(k+2), ... u(k+w).q  Actuator applies u(k).

Ø  Buffered actuationq  Actuator buffers previous control inputs u(k+1), ... u(k+w).q  Applies buffered control input if updated input is lost. q  A control horizon of w+1 à may tolerate w consecutive packet loss.

ModelPredictiveControl

ExtendedKalman Filter

Actuators

Sensors

Reference

[ ( ), ( 1), , ( )]u k u k u k w+ +, (, (, (, (

( )y kˆ( )y k

ˆ( )x k

Buffer

Plant

ˆ( )u k

Pump 1

Heater

1L

2L

Tank 1

Tank 2

Reagent Tank 2

2u

b

Reagent Tank 1

1u

a

Pump 2

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ExothermicReac1onPlantPlant: nonlinear chemical reaction Control input: u1 and u2 Objective: Maintain temperature in Tank 2

Wireless Cyber-Physical Simulator (WCPS)•  Integrate TOSSIM and Simulink

•  Capture dynamics of both wireless networks and physical plants

•  Holistic simulations of wireless control

•  Open source: wcps.cse.wustl.edu

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ResilienceofStateObserver

Left: without state observer

Without state observerunder 60% sensor data loss

Extended Kalman Filter (EKF) under 60% sensor data loss

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ResilienceofBufferedActua1on

System is more sensitive to data loss to actuators than that from sensors.

Actuation buffer (size 8) under 60% controller data loss

Tradi1onalRou1ngØ  Entire network uses uniform routing strategiesØ  Source routing: single path routing

q  Efficient but unreliable

Ø  Graph routing: every node on the primary path has a back path.

q  More reliable at cost of capacity and energy

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Gateway 1 Gateway 2

Sensor(a) Source Routing (b) Graph Routing

1, 2

3, 4

3

4(5)

Primary Path

Backup Path

5 (6)5, 67

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Gateway 1 Gateway 2

Sensor

1, 2

3, 4

5, 6

AsymmetricRou1ng

Ø Differentiated routing based on control needs

Ø  State observer handles data loss from sensors à Ø  Source routing from sensors

q  State observer compensates for lower reliabilityq  Cost less resource

Ø Actuation is more sensitive to data loss à Ø Graph routing to actuators

q  Higher reliabilityq  Higher resource cost needed by control

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Tailoring routing strategies for control design à Efficient and Resilient Wireless Control

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MaximumAbsoluteError

Ø  Source/Graph performs close to Graph routing under 3HzØ  Source/Graph’s resource efficiency allows 5Hz control

q  Further improve control performance

-73dBmNoise -73dBmNoise

B. Li, Y. Ma, T. Westenbroek, C. Wu, H. Gonzalez and C. Lu, Wireless Routing and Control: a Cyber-Physical Case Study, ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS’16).

TowardsDependableWirelessControl

Ø Real-time, predictable wireless networkingq Protocols and delay analysis for bounded latency

Ø Optimize control performance over wirelessq  Incorporate scheduling analysis in rate selection for

wireless control

Ø Resilient wireless control under data lossq Tailor routing strategies for control needs

Ø Cyber-physical co-design helps overcome the dependability challenges!

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EngineeringBuildingBlocks

Ø Industrial wireless networks have arrivedq  Industrial standards: WirelessHART, ISA100q World-wide field deploymentsq Great opportunity for the sensor networks community!

Ø WirelessHART implementation in TinyOS

Ø WCPS: Wireless Cyber-Physical Simulatorq Enable holistic simulations of wireless control systems

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FutureDirec1ons

Ø Scaling up wireless control networksq From 100 nodes à 10,000 nodes

q Dealing with dynamics locallyq Hierarchical or decentralized architecture

q Predictable protocols over low-power wide-area networks

Ø Unified science and engineering of wireless controlq Case studies à unified theory, architecture and

methodology of cyber-physical co-design.

q Bridge the gap between theory and systems

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ForMoreInforma1on

Ø  C. Lu, A. Saifullah, B. Li, M. Sha, H. Gonzalez, D. Gunatilaka, C. Wu, L. Nie and Y. Chen, Real-Time Wireless Sensor-Actuator Networks for Industrial Cyber-Physical Systems, Special Issue on Industrial Cyber-Physical Systems, Proceedings of the IEEE, accepted.

Ø  Research on Real-Time Industrial Wireless Sensor-Actuator Networks: http://cps.cse.wustl.edu/index.php/Real-Time_Wireless_Control_Networks

Ø  Wireless Cyber-Physical Simulator: http://wcps.cse.wustl.edu

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