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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?