3/15/2010 1 Introduction to Networked Control Systems Introduction to Networked Control Systems Vijay Gupta HYCON‐EECI Graduate School on Control 2010 15‐19 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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3/15/2010
1
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
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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gwireless sensors • Reconfiguring when new drive is bored
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