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3/15/2010
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Introduction to Networked Control SystemsIntroduction to Networked Control Systems
Vijay Gupta
HYCON‐EECI Graduate School on Control 201015‐19 March 2010
Karl H. Johansson
University of Notre Dame
U.S.A.
Royal Institute of Technology
Sweden
Course Instructors
Vijay Gupta, U Notre Dame
• B. Tech, IIT Dehli, EE
• MS, PhD, Caltech, EE
• Postdoc, U Maryland
Karl H. Johansson, KTH
• MS, PhD, Lund U, EE
• Postdoc, UC Berkeley
Research interests
• networked control systems
• sensor networks
• distributed estimation and detection
• usage‐based value of information
Research interests
• networked control systems
• hybrid systems
• control applications in automotive, automation and communication systems
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Goals• Review recent technology trends and applications in control that motivate networked control systems
• Provide an overview of basic tools from communications, computer science and control theory that can be used for further studies
• Review recent results in distributed estimation and control, packet‐based estimation and control, control i f ti ti t b d t lin presence of quantization, event‐based control
• Discuss open research problems and emerging networked control applications
Lectures OutlineMon Tue Wed Thu Fri
9:00 L1: Introduction L5: Suboptimaldi ib d
L7: I f i
L11: Control L13: Control i h li i ddistributed
controlInformation theory
across networks
with limited processing
10:30 Break
11:00 L2: Background L6: Sensor fusion
L8: Control across channels
L12: Event‐based control
L14: Summary and future directions
12:30 Lunch
14 00 L3 I f i L9 M k14:00 L3: Informationpatterns
L9: Markovjump linear systems
15:30 Break
16:00 L4: Optimaldistributed control
L10: Control across erasure channels
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OutlineLecture 1: Introduction—what are networked control systems, why are they
important, what are the challenges, what is the scope of the course?
Lecture 2: Background—LQG, Kalman filtering, graph theory
Lecture 3: Information patterns, Witsenhausen’s counterexample
Lecture 4: Optimal control for special topologies—quadratic invariance
Lecture 5: Suboptimal control—consensus, distributed receding horizon control
Lecture 6: Sensor fusion and distributed estimation
Lecture 7: Information theory, fundamental limits, control across noisy channels
Lecture 8: Control across digital noiseless channels
Lecture 9: Markov jump linear systems
Lecture 10: Control across erasure channels
Lecture 11: Control across networks, multiple sensors
Lecture 12: Event‐based control
Lecture 13: Control with limited processing
Lecture 14: Summary and future directions
Material and web page
• The course is reflected by that networked control i i ith t ltis an emerging area with many recent results
• The course is similar to the HYCON‐EECI 2008 and 2009 courses by Vijay Gupta and Richard Murray
• See lecture material and references for furtherSee lecture material and references for further reading at
http://www.cds.caltech.edu/~murray/wiki/HYCON‐EECI,_Spring_2009
• Some material will be handed during the week
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Lecture 1: Introduction • Course outline and logistics
• What is a networked control system?What is a networked control system?
• Motivating applications
• What are the challenges?
Networked control system
Control system with sensor, controller and actuator devices connected through a networkdevices connected through a network
Sensors Controllers Actuators
Wired or wireless communication links
Plant
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Networked control architectures
PlC SAPlant
C
SAPlantC SA
SA CPlant
Network
Plant
C
SAPlant
C
SA
A history of control
[Baillieul & Antsaklis, 2007]
Wireless control
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Communication in process industry
Wireless systems benefit from
[ISA100]
WirelessHARTISA100ZigBee…
• Lower installation and maintenance costs
• Increased sensing capabilities and flexibility
Major consequence for control system architecture
But, there are also early examples
• Adaptive control of an Orecrusher in Kiruna (northern Sweden) in 1973( )
• Control computer located in Lund (southern Sweden) 1800 km away
• Sensor data and control commands were sent over the public telephone net with sampling interval of 20 s
© Karl H. Johansson, Wireless controlBorisson and Syding, Automatica, 11, 1975
sampling interval of 20 s
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Emerging networked control systems
Transportation networksPower networksTelecommunication
Building automation Environmental monitoringProcess industryg
Lecture 1: Introduction • Course outline and logistics
• What is a networked control system?What is a networked control system?
• Motivating applications– Mining
– Process industry
– Transportation
– Aerospace
• What are the challenges?
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Mining industryMining industry
– Mining and smelting companyg g p y– 3 500 MEUR turnover– 4 500 employees– 3 Swedish and 1 Irish mine
Garpenberg mineMi i i 9th– Mining since 9th century
– 1000K ton oar/yr• 58K Zn, 21K Pb, 0.56K Cu, 0.1K Ag, 0.2 Au
– 1 100 m deep– 280 employees
MiningMiningProcessProcess
Mining phases:ll d bl• Drilling and blasting
• Ore transportation• Ore crushing
• Ventilation represents 50% of electric power consumption
• Ventilation feedback control systemsare often poor or non-existing
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Wireless networking in Wireless networking in mining ventilation control mining ventilation control
Ventilation control through • Mining is a highly automated process
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The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again.
gwireless sensors • Reconfiguring when new drive is bored
© Karl H. Johansson, KTH
Control architecture and objectivesControl architecture and objectives
Turbine- Ventilation Fan Tubes-
Primary system Secondary system
Objectives:– Control air quality (O2, NOx and CO) in extraction rooms at suitable level
Controller Turbineheater
Ventilationshaft
ControllerNetwork
Fannetwork
Tubesrooms
PressureWSN
MobileWSN
q y ( 2, )• Regulate turbine and heater to provide suitable airflow pressure at ventilation fans• Regulate ventilation fans to ensure air quality in extraction rooms
– Safety through wireless networking for personal communication and localization
Design constraints:– Physical interconnections, actuators limitations and networking capabilities – Sensing capabilites: O2, NOx, COx, pressure and temperature
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Control of froth flotation process
• Froth flotation process concentrates the metal‐bearing mineral in the ore
Minerals
Ore
Waste
• Level and flow sensors are used for regulating flotation process using SISO PID control
Wireless control of flotation process
flotation process using SISO PID control
• Wireless sensors enable more flexible control strategies and lower costs for maintenance and upgrades
MineralsController
Ore
Waste
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Today’s industrial communication architecture
Workplaces
Remote ClientsCentralized control system with low‐level loops closed over wired network
Workplaces
Control Network
System Servers
Process Automation Process Automation and Safety
Operator EngineeringMaintenance
SafetyControllersControllers
MCC
Variable Speed Drives
S800 I/O
S900 I/O (Ex)
Sensors
Actuators
• Local control loops closed over wireless multi‐hop network
• Potential for a dramatic change:f d h h l l d fl bl d b d
Future wireless control architecture
– From fixed hierarchical centralized system to flexible distributed
– Move intelligence from dedicated computers to sensors/actuators
Smart Actuator
Smart Sensor
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WirelessHARTWireless networking protocol standard (2007) designed for sensing and control applications
10 ms TDMA and CSMA time slotsPeriodic superframes of N slots
Improved Vehicle Control Through Vehicle‐to‐Vehicle and Vehicle‐to‐Infrastructure Communication
• Fuel‐optimal speed for a heavy vehicle depends on the road grade and other traffic conditions
• Information from internal and external sensors
Per Sahlholm, 2008‐12‐05
together with other vehicles and infrastructure enable much better control strategies
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Improved fuel efficiency through Improved fuel efficiency through predictive control and sensor fusionpredictive control and sensor fusion
• Predict driving conditions based on networked sensing systems
© Karl H. Johansson, KTH
– E.g., GPS, RDS congestion info, off- and onboard sensors• Control vehicle subsystems to improve fuel efficiency
– E.g., cruise control, automated gear shifting, auxiliary systems
[Pettersson & J., IJC, 2006]
Single vehicle road grade estimationSingle vehicle road grade estimation
– Road grade needed for predictive control– Not provided by digital maps
Altitude from GPS
p y g p
© Karl H. Johansson, KTH[Sahlholm et al., AAC, 2007]
Altitude estimate from wheel velocity
With GPS
Without GPS
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NetoworkedNetoworked control architecture in a control architecture in a ScaniaScania trucktruck
© Karl H. Johansson, KTHElectronic Control Units connected over 3 Controller Area Networks
Control of the SMART‐1 spacecraft
• First European lunar mission, launched Sep 2003
• Go to the moon using electric primary propulsion• Go to the moon using electric primary propulsion– Thrust 68 mN, 410 days to reach moon orbit
Sun sensorsSun sensorsSun sensors(3 in total) Star trackers (2 in total)
Hydrazine thrusters(4 in total)
Reaction wheels(4 in total)
EP thruster and orientationmechanism
Sun sensors(3 in total) Star trackers (2 in total)
Hydrazine thrusters(4 in total)
Reaction wheels(4 in total)
EP thruster and orientationmechanism
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• Moon capture
Hybrid control of orbit and attitude
Detumble Mode Safe Mode
Bodin, Swedish Space Agency, 2005
Switch between different control configurations depending on commands and autonomous actions
Attitude control system under operation in Science mode
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Networked control architecture of the SMART‐1 spacecraft
Power
EarthcommunicationControl system
Karl H. Johansson, CTS‐HYCON Workshop, Université Paris Sorbonne, 2006
PayloadActuatorsSensors
Bodin, Swedish Space Agency, 2005
Networked control architecture gives efficient development and flexible operation
Networked control architecture of the SMART‐1 spacecraft
Wired and wireless communication systems influence control performance
PowerActuators Sensors Payload Earthcommunication
Controlcomputers
Network
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Lecture 1: Introduction • Course outline and logistics
• What is a networked control system?What is a networked control system?
• Motivating applications
• What are the challenges?
Experimental setup for demo oncontrol over multi‐hop network
Physical Process 2
Hop Node
Physical Process 1
Control Signals
Control SignalsMeasurementsMeasurements
Meta NodeController
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Effect of packet loss
Poor performance
PACKET LOSS = 14% PACKET LOSS = 45%Noisy Control Signal
5 m 10 m 15 m 16 m
Sensor-controller distance
5 m 10 m 15 m 16 m
Uncertainty and variations in wireless communication
Packet reception ratevs Distance Mean bit error rate Packet loss rate
Application
Zuniga & Krishnamachari, SECON, 200440 m 4 h 4 h
Willig et al., IEEE Trans. Ind. Electron., 49, 6, 2002
Packet reception rate Large variations in
Physical
Datalink
Network
Transport
Park et al., KTH, 20074 min
• Connectivity• Bit and packet delivery• End-to-end delivery
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How trade‐off network resources and control performance?
How unify time‐ and event‐driven communication‐control?
How move intelligence from central units to local devices?
Challenges for networked control
How move intelligence from central units to local devices?
How distribute computing load between computers and how let multiple computers cooperate in a shared task?
How handle communication limits and dropped packets?
What types of protocols should be used for communication?
Sensors Controllers Actuators
Plant
A communication, computing or control problem?
Approaches to networked control:
1. Communication protocol suitable for control Control1. Communication protocol suitable for control
2. Controller that compensates for computing and communication imperfections
3. Integrated C3 co‐design MAC
NET
ControlApplication
PHY
Plant
Controller
SensorActuator
Wireless network
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Summary Lecture 1: Introduction • Course outline and logistics
• What is a networked control system?What is a networked control system?
• Motivating applications
• What are the challenges?
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