Real-Time Wireless Control Networks for Cyber-Physical Systems - - PowerPoint PPT Presentation

Real-Time Wireless Control Networks for Cyber-Physical Systems

Chenyang Lu

Cyber-Physical Systems Laboratory Department of Computer Science and Engineering

sensor data

Sensor Actuator

control command

Wireless Control Networks

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Ø Real-time Ø Reliability Ø Control performance Controller

Wireless for Process Automation

Ø World-wide adoption of wireless in process industries

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1.5+ billion hours

• pera6ng experience

100,000s of smart wireless field devices 10,000s of wireless field networks

Courtesy: Emerson Process Management

Offshore Onshore

Killer App of Sensor Networks!

WirelessHART

Industrial wireless standard for process monitoring and control

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Ø Industrial-grade reliability

q Multi-channel TDMA MAC q One transmission per channel q Redundant routes q Over IEEE 802.15.4 PHY

Ø Centralized network manager

q collects topology information q generates routes and

transmission schedule

q changes when devices/links break

Our Endeavor

• 1. Real-time scheduling theory for wireless
• 2. Wireless-control co-design
• 3. Case study: wireless structural control

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Real-Time Scheduling for Wireless

Goals Ø Real-time transmission scheduling à meet end-to-end deadlines Ø Fast schedulability analysis à online admission control and adaptation Approach Ø Leverage real-time scheduling theory for processors Ø Incorporate unique wireless characteristics Results Ø Fixed priority scheduling

q Delay analysis [RTAS 2011, TC, RTSS 2015] q Priority assignment [ECRTS 2011]

Ø Dynamic priority scheduling

q Conflict-aware Least Laxity First [RTSS 2010] q Delay analysis for Earliest Deadline First [IWQoS 2014]

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Real-Time Flows

Ø Flow: sensor à controller à actuator over mul6-hops Ø A set of flows F={F1, F2, …, FN} ordered by priori6es Ø Each flow Fi is characterized by

q A source (sensor), a des6na6on (actuator) q A route through the controller q A period Pi q A deadline Di ( ≤ Pi) q Total number of transmissions Ci along the route

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highest lowest priority

Scheduling Problem

Ø Fixed priority scheduling

q Every flow has a fixed priority q Order transmissions based on the priorities of their flows.

Ø Flows are schedulable if delayi ≤ Di for every flow Fi Ø Goal: efficient delay analysis

q Gives an upper bound of the end-to-end delay for each flow q Used for online admission control and adaptation

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end-to-end delay of Fi deadline of Fi

End-to-End Delay Analysis

Ø A lower priority flow is delayed due to

q channel contention: all channels in a slot are assigned

to higher priority flows

q transmission conflict involving a same node

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2 1 1 and 5 are conflicting 4 and 5 are conflicting 4 5 3 3 and 4 are conflict-free

Ø Analyze each type of delay separately Ø Combine both delays à end-to-end delay bound

Insights

Ø Flows vs. Tasks

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Similar: channel contention

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Different: transmission conflict

Ø Channel contention à multiprocessor scheduling

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A channel à a processor

q

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

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!"#$%&'"(!"# &!"#$%&'"(!"$

)*$%+*,

Delay due to Conflict

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

q Q(l,h) = 5 à Fh can delay Fl

by 5 slots

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Fl delayed by 2 slots Fl delayed by 2 slots Fl delayed by 1 slot

WirelessHART Tested

Ø Implementation on a testbed of 69 TelosB motes. Ø WirelessHART stack on TinyOS/mote. Ø Network manager (scheduler + routing).

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• M. Sha, D. Guna6laka, C. Wu and C. Lu, Implementa6on and Experimenta6on of Industrial Wireless Sensor-

Actuator Network Protocols, European Conference on Wireless Sensor Networks (EWSN), February 2015.

Outline

Ø WirelessHART: real-time wireless in industry Ø Real-time scheduling theory for wireless Ø Wireless-control co-design Ø Case study: wireless structural control

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Wireless-Control Co-Design

Challenge Ø Wireless resource is scarce and dynamic Ø Cannot afford separating wireless and control designs Cyber-Physical Systems Approach Ø Holistic co-design of wireless and control Examples Ø Rate selection for wireless control [RTAS 2012, TECS] Ø Wireless structural control [ICCPS 2013] Ø Wireless process control [ICCPS 2015]

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Goal: opAmize control performance over wireless

Rate Selection for Wireless Control

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

Control Performance Index

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Ø Digital implementation of control loop i

q

Periodic sampling at rate fi

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Performance deviates from continuous counterpart

αi e−β i fi

Ø Overall control cost of n loops:

αi e−β i fi

i=1 n

∑

αi, βi

delayi ≤1/ fi

The Rate Selection Problem

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f = { f1, f2,, fn}

fi

min ≤ fi ≤ fi max

minimize control cost

αi e−β i fi

i=1 n

∑

subject to Ø Constrained non-linear optimization Ø Determine sampling rates Delay bound

Polynomial Time Delay Bounds

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

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q

Non-linear

q

Non-convex

q

Non-differentiable

Lagrange dual of objec6ve R a t e

• f

c

• n

t r

• l

l

• p

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Wireless-Control Co-Design

Relax delay bound to simplify optimization

Ø Derive a convex and smooth, but less precise delay bound. ➠ Rate selection becomes a convex optimization problem.

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Control cost

Evaluation

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q

Greedy heuristic is fast but incurs high control cost.

q

Subgradient method is neither efficient nor effective.

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Simulated annealing incurs lowest control cost, but is slow.

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Convex approximation balances control cost and execution time.

5 10 15 20 25 30 5 10 15 20 25 30 Control Cost Number of Control Loops Greedy Heuristic Subgradient Convex Approximation Simulated Annealing 5 10 15 20 25 30 10

−2

10 10

2

10

4

10

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Execution Time (seconds) Number of Control Loops Greedy Heuristic Subgradient Convex Approximation Simulated Annealing

Wireless Structural Control

Ø Structural control systems protect civil infrastructure. Ø Wired control systems are costly and fragile. Ø Wireless structural control achieves flexibility and low cost.

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Heritage tower crumbles down in earthquake of Finale Emilia, Italy, 2012. Hanshin Expressway Bridge ader Kobe earthquake, Japan, 1995.

Contributions

Ø Wireless Cyber-Physical Simulator (WCPS)

q Capture dynamics of both physical plants and wireless networks q Enable holistic, high-fidelity simulation of wireless control systems q Integrate TOSSIM and Simulink/MATLAB q Open source: http://wcps.cse.wustl.edu

Ø Realistic case studies on wireless structural control

q Wireless traces from real-world environments q Structural models of a building and a large bridge q Excited by real earthquake signal traces

Ø Cyber-physical co-design

q End-to-end scheduling + control design q Improve control performance under wireless delay and loss

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• B. Li, Z. Sun, K. Mechitov, G. Hackmann, C. Lu, S. Dyke, G. Agha and B. Spencer, Realis6c Case Studies of Wireless Structural Control,

ACM/IEEE Interna6onal Conference on Cyber-Physical Systems (ICCPS'13), April 2013.

Bill Emerson Memorial Bridge

Ø Main span: 1,150 ft. Ø Carries up to 14,000 cars a day over Mississippi. Ø In the New Madrid Seismic Zone Ø Replaced joints of the bridge by actuators

q 24 hydraulic actuators

Ø Vibration mode:

q 0.1618 Hz for 1st mode q 0.2666 Hz for 2nd mode q 0.3723 Hz for 3rd mode

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(a) (b)

Jindo Bridge: Wireless Traces

Ø Largest wireless bride deployment [Jang 2010]

q 113 Imote2 units; Peak acceleration sensitivity of 5mg – 30mg

Ø RSSI/noise traces from 58-node deck-network for this study

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Reduction in Max Control Power

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Cyber-physical co-design à 50% reduc6on in control power.

Conclusion

Ø Real-time wireless is a reality today

q Industrial standards: WirelessHART, ISA100 q Field deployments world wide

Ø Real-time scheduling theory for wireless

q Leverage real-time processor scheduling q Incorporate unique wireless properties

Ø Cyber-physical co-design of wireless control systems

q Rate selection for wireless control systems q Scheduling-control co-design for wireless structural control

Ø WCPS: Wireless Cyber-Physical Simulator

q Enable holistic simulations of wireless control systems q Realistic case studies of wireless structural control

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Future Directions

Ø Scaling up wireless control networks

q From 100 nodes à 10,000 nodes q Dealing with dynamics locally q Hierarchical or decentralized architecture

Ø Science and engineering of wireless control

q Case studies à unified theory, architecture and methodology q Bridge the gap between theory and systems q Textbook on cyber-physical co-design

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For More Information

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 Real-Time Industrial Wireless Sensor-Actuator Networks: http://cps.cse.wustl.edu/index.php/Real-Time_Wireless_Control_Networks Ø Wireless Structural Health Monitoring and Control: http://bridge.cse.wustl.edu Ø Wireless Cyber-Physical Simulator: http://wcps.cse.wustl.edu

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