control engineering laboratory helsinki university of technology system level simulation of wireless...

Control Engineering LaboratoryHelsinki University of Technology

System level simulation ofwireless networked control systems

Simulation and implementation platform: PiccSIM

Lasse Eriksson

Shekar Nethi (UV), Mikael Pohjola (TKK/CEL) and Prof. Riku Jäntti (TKK/COM)


Outline

• Introduction and motivation• Wireless automation• Design tools• PiccSIM-platform• Case studies• Demo


Introduction

• Old story short:– More computing power available– Smaller and smaller devices (MEMS/NEMS)– Networking capabilities, wireless technology

=> ubiquitous computing systems


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

Possibly wireless signal

Analog signal

ActuatorContinuous-Time Plant

Sampling (h)Sensor

Discrete-TimeController

ControlNetwork

sck

ControlNetwork

cak

Placed together

ck


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


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.


Towards reliable wireless automation

Quality of service

Increase robustnessDecrease jitter

Requirement for control

Performance of Wireless networks

Increase jitter marginand tolerance to errors

Data fusionPID Controller tuningNew control algorithms

Coexistence protocolsMulti-path routing (mesh)Synchronization

Wireless automation systems


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 (Nordite-program)


Some architectures

Sink / Controller / CoordinatorActuator

Sensor node Measurement packet Action packet

Sink / Controller / CoordinatorActuator

Sensor node Measurement packet Action packet

Sink / Controller / CoordinatorActuator Sink / Controller / CoordinatorSink / Controller / CoordinatorActuatorActuator

Sensor node Measurement packet Action packetSensor nodeSensor node Measurement packetMeasurement packet Action packetAction packet

Semi-automated architecture Automated architecture


More architectures

Process

Data fusionPID Controller

Actuator


Actuator

MPC Coordinator

MPC Coordinator

Actions

Actions

Ref

Ref


Actuator


Actuator

PIDController

+_

yr(t)

τ(t)

( ) ( ), ( ),

( ) ( ), ( ),

x t f x t u t t

y t g x t u t t

Process Sensor

DataFusion

Wireless Network

e(t) u(kh)

ˆ( )y t

y1(t)

yN(t)

Act.


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)


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

Communications Systems:

- Ns2, OPNET, QUALNET, SENSE, etc.

PiccSIM = MoCoNet + Ns2

PiccSIM


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 user-specified 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


The system consists of three computers

• 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)

PiccSIM = MoCoNet + Ns2

• Router: All computers are connected through a network router

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.


An example• 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


Network configuration tool


Network configuration


Network layout


TCL script generation

Ns-2


Simulation case studies• Building Automation• Factory floor• Target tracking and control

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

Performance comparison of AODV (Single path) and LMNR (Multipath Routing protocol) in different scenarios of industrial wireless systems


Multi-path routing• LMNR (Localized Multiple next

hop routing)

– 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

AODV AOMDV

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


Building Automation

Physical Models:

•Heat balance in rooms (PID control)

•CO2 concentration in rooms (relay control)

•Event driven signals, lighting (on/off)

Communication Model:

•Zigbee motes (15m range)

•Ricean propagation channel

TzdTz/dt

CzdCz/dt

2

Cz [ppm]

1

Tz [C]

-K-

roo_a*c_a

-K-

occ+lighting+computer

nv

nv

-K-

UA_W

-K-

UA_S

-K-

UA_R

-K-

UA_N

-K-

UA_E

Tw - Tz

Tsa - Tz

Ts - Tz

To - Tz

Tn - Tz

Te - Tz

Product1

Product

1s

Integrator1

1s

Integrator

-K-

Cp_dot

-K-

C_sa

Add2

Add1

Add

-K-

1/delta

-K-

1/V_z

9

Np

8

To [C]

7

Tw [C]

6

Te [C]

5

Ts [C]

4

Tn [C]

3

Tsa [C]

2

f_s,z [m3/s]

1

q(t),heat [W]


Results (LMNR vs. AODV)Packet Delivery ratio (%)

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

Results clearly indicate that multipath routing has contributed to increased packet delivery ratio and decreased jitter (delay variance)


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


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


Results (LMNR vs. AODV)

Delayed information may be outdated:Significant improvement in average end-to-end delay for LMNR over 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 (%)

Avg. end-to-end delay (sec)


Target Tracking and Path Management• 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 Outage time and Error estimate


Recorded simulation for Target tracking


Questions and Answers?

Contact information:Lasse ErikssonHelsinki University of TechnologyControl Engineering LaboratoryP.O.Box 5500FI-02015 TKKFinland

Tel. +358 50 384 1715Email: [email protected]

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