Transcript Slide 1

System level simulation of
wireless networked control systems
Simulation and implementation platform: PiccSIM
Lasse Eriksson
Shekar Nethi (UV), Mikael Pohjola (TKK/CEL) and
Prof. Riku Jäntti (TKK/COM)
Control Engineering Laboratory
Helsinki University of Technology
Outline
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Introduction and motivation
Wireless automation
Design tools
PiccSIM-platform
Case studies
Demo
Control Engineering Laboratory
Helsinki University of Technology
Introduction
• Old story short:
– More computing power available
– Smaller and smaller devices (MEMS/NEMS)
– Networking capabilities, wireless technology
=> ubiquitous computing systems
Control Engineering Laboratory
Helsinki University of Technology
Introduction...
• Networked control systems are real-time
computing and control systems
– Sensors, actuators and controllers communicate over
a shared medium
– Field buses frequently used in control applications
Analog signal
Actuator
ContinuousTime Plant
Sampling (h)
Sensor
Possibly wireless signal
Placed together
Control ca

Network k
 kc
Discrete-Time
Controller
Control Engineering Laboratory
Helsinki University of Technology
Control sc

Network k
Wireless automation
• Wireless technology has already changed the consumer
markets (cell phones, PDAs, laptops...)
• Wireless automation: Embedded and networked control
systems where the different devices (sensors, controllers
and actuators) communicate seamlessly using wireless
technology
• Wireless vision: autonomic communications and
computing gets rid of the human-in-the-loop by making
the systems self-configuring, self-healing, self-optimizing
and self-protecting
Control Engineering Laboratory
Helsinki University of Technology
Wireless automation...
• Opportunities and challenges?
– Connection of field devices through a field bus
requires a lot of network planning, wiring and
troubleshooting as a result, for many automation
systems the cost is “all in the wires”
=> wireless provides flexibility, reconfigurability, better
capabilities for fault diagnostics, and savings in wiring
– There are issues regarding e.g. security (out of the
scope of this study)
– The special characteristics of wireless networking
need to be addressed in control design!
=> varying time-delays, packet losses etc.
Control Engineering Laboratory
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Towards reliable wireless automation
Quality of service
Requirement for
control
Data fusion
Increase jitter margin
PID Controller tuning
and tolerance to errors
New control algorithms
Wireless automation systems
Performance of
Wireless networks
Control Engineering Laboratory
Helsinki University of Technology
Increase robustness Coexistence protocols
Decrease jitter
Multi-path routing (mesh)
Synchronization
WISA-project (2006-2007)
• Wireless sensor and actuator networks for
measurement and control
– University of Vaasa (prof. Riku Jäntti,
Communications group)
– Helsinki University of Technology (prof. Heikki Koivo,
Control Engineering Laboratory)
– Royal Institute of Technology (prof. Mikael
Johansson, Automatic Control group and prof. Jens
Zander, Radio Communication Systems group)
• Funded by TEKES and Vinnova (Norditeprogram)
Control Engineering Laboratory
Helsinki University of Technology
Some architectures
Semi-automated architecture
Automated architecture
Sink / Controller / Coordinator
Actuator
Sensor node
Measurement packet
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Action packet
More architectures
Data fusion
PID Controller
Actuator
Actions
Ref
MPC
Coordinator
Process
Ref
Actions
Data fusion
PID Controller
Actuator
yr(t)
y1(t)
+
_
e(t)
u(kh) A
PID
c
Controller
t.
Sensor
Data
Fusion
Process

 x(t )  f  x(t ), u (t ), t 


 y(t )  g  x(t ), u(t ), t 
yN(t)
τ(t)
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Helsinki University of Technology
Wireless Network
yˆ (t )
Tools for design?
• There is a lack of design tools that are able to
deal with integrated communication and control
systems
• TrueTime (Lund University): Network simulation
with MATLAB/Simulink
– Accuracy of network simulation?
– Few network protocols available
– Good for control performance analysis (Jitterbug)
Control Engineering Laboratory
Helsinki University of Technology
We need to find a common testing platform for
Communication and Control Design
Option 1: Develop a New Simulator
(example: Java or MATLAB based
simulators)
Option 2: Integrate existing available
simulators
Control Design:
- MATLAB/Simulink/xPC Target (automatic
code generation), MoCoNet-platform
PiccSIM
Communications Systems:
- Ns2, OPNET, QUALNET, SENSE, etc.
PiccSIM = MoCoNet + Ns2
Control Engineering Laboratory
Helsinki University of Technology
PiccSIM
• Platform for integrated communications and control
design, simulation, implementation and modeling =
PiccSIM
• The key features of the platform are
– Support for powerful control design and implementation tools
provided by MATLAB enabling automatic code generation from
Simulink models for real-time execution
– real-time control of a true or simulated process over a userspecified network
– capability to emulate any wired/wireless networks readily
available in Ns2
– easy-to-use network configuration tool
– the platform is accessible over the Internet
Control Engineering Laboratory
Helsinki University of Technology
PiccSIM = MoCoNet + Ns2
The system consists of three computers
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Webserver, Database, xPC Host: The
server computer is responsible for
maintaining connections between users
and processes, running a reservation
system for controlling processes.
RTOS xPC Target: The computer
controls the real process or simulates a
process in real-time. Equipped with an
I/O controller board.
Network simulator (Ns-2)
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Router: All computers are connected through a network router
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S. Nethi, M. Pohjola, L. Eriksson, R. Jäntti. Platform for Emulating Networked
Control Systems in Laboratory Environments, to appear in Proc. IEEE
International Symposium on a World of Wireless, Mobile and Multimedia
Networks (IEEE WoWMoM 2007), Helsinki, Finland, June 18-21, 2007.
Control Engineering Laboratory
Helsinki University of Technology
An example
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Two computers (xPC Target and
Ns2)
UDP packets are generated from
the signal measured from the
process.
Packets are sent on to the
network
Ns2 computer using TAP agent
captures packets and then node
mapping is done using UDP port
numbers
• On successful reception the packet is sent back to xPC Target
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Network configuration tool
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Network configuration
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Network layout
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TCL script generation
Ns-2
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Simulation case studies
• Building Automation
• Factory floor
• Target tracking and control
Performance comparison of AODV (Single path) and LMNR (Multipath Routing
protocol) in different scenarios of industrial wireless systems
S. Nethi, M. Pohjola, L. Eriksson, R. Jäntti. Simulation case studies of wireless
networked control systems, submitted to the 10th ACM/IEEE International
Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems
(MsWIM’2007), Crete Islands, Greece, October 22-26, 2007
Control Engineering Laboratory
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Multi-path routing
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LMNR (Localized Multiple next
hop routing)
AODV
AOMDV
– Set up multiple routes
– Next hop is locally decided
based on load, interference,
and link availability
=> Increase robustness against
link faults (decrease the need
for rerouting in case of failures)
LMNR
S. Nethi, C. Gao and R Jäntti, “Localized Multiple Next-hop Routing
Protocol”, to appear in Proc. 7th international conference on ITS
telecommunication (ITST 2007), Paris, France, June 5-8, 2007
Control Engineering Laboratory
Helsinki University of Technology
Building Automation
Physical Models:
•Heat balance in rooms (PID control)
•CO2 concentration in rooms (relay
control)
•Event driven signals, lighting (on/off)
Communication Model:
1
q(t),heat [W]
-K-
•Zigbee motes (15m range)
occ+lighting+computer
2
f_s,z [m3/s]
Add2
3
Product
Tsa [C]
4
-K-
•Ricean propagation channel
dTz/dt
-K-
Tsa - Tz roo_a*c_a
Tn [C]
1/delta
-KTn - Tz
1
s
Tz
1
Tz [C]
Integrator
UA_N
5
Ts [C]
6
-KTs - Tz
Te [C]
Add
UA_S
-K-
Te - Tz
UA_E
7
Tw [C]
-KTw - Tz
UA_W
8
To [C]
-KTo - Tz
UA_R
-KdCz/dt
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C_sa
-K-
9
Np
-K-
Cp_dot
1/V_z
nv
Add1
Product1
nv
1
s
Integrator1
Cz
2
Cz [ppm]
Results (LMNR vs. AODV)
Packet Delivery ratio (%)
Results clearly indicate that
multipath routing has contributed
to increased packet delivery ratio
and decreased jitter (delay
variance)
Avg. end-to-end delay and jitter (sec)
To improve system
performance:
-Utilize group coordination and
data aggregation to localize
computation and decrease network
traffic
- Redesign of network, i.e. adding
more access points
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Helsinki University of Technology
Factory floor
• Wireless measurement system used for monitoring a
complex industrial process (on the top of the control
system)
• Optimization (coordination) of process performed
• Time based (control) and event based (alarms) traffic
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Factory floor...
• Demanding environment for wireless
communication
– The frequency and time dependent shadow fading
caused by the environment can cause certain
frequency bands to become unusable, leading to link
failure
– Moving objects in the environment change the
channels’ quality
• The interesting metric to investigate is the QoS
(i.e. delay bounds and packet loss) of the
wireless monitoring system
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Results (LMNR vs. AODV)
As the shadowing deviation increases,
so does the probability of link failure.
LMNR shows robustness against link
failures.
Packet Delivery ratio and Normalised
routing overhead (%)
Delayed information may be outdated:
Significant improvement in average endto-end delay for LMNR over AODV.
Control Engineering Laboratory
Helsinki University of Technology
Avg. end-to-end delay (sec)
Target Tracking and Path Management
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Two Communication pairs:
- Sensors-Controller
- Controller-Mobile Node
Propagation model:
- Two ray ground model
Results produced for 9 different reference
paths
Packet delivery fraction and Avg. end-to-end delay
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Helsinki University of Technology
Outage time and Error estimate
Recorded simulation for Target tracking
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Helsinki University of Technology
Questions and Answers?
Contact information:
Lasse Eriksson
Helsinki University of Technology
Control Engineering Laboratory
P.O.Box 5500
FI-02015 TKK
Finland
Tel. +358 50 384 1715
Email: [email protected]
Control Engineering Laboratory
Helsinki University of Technology