Wireless Control Systems - from theory to a tool chain, Mikael Björkbom, Aalto University

Royal Institute of Technology KTH

Wireless Control Systems

- from theory to a tool chain

Aalto University

Department of Communications and Networking

Control Engineering Group

KTH

Radio Communication Systems Group

Automatic Control Group

Mikael Björkbom

Wireless Sensor and Actuator Networks for Measurement and Control

Phase II


Wireless Automation: Control

Communication affects control performance

-> Control should be robust to problems in the network


Nordite WISA Project

Quality of service

Increase robustness

Decrease jitter

Requirements for

current control algorithms

Performance of

current wireless networks

Increase jitter margin

and tolerance to errors

Data fusion

PID Controller tuning

New control algorithms

Coexistence protocols

Multi-path routing (mesh)

Synchronization

Wireless automation systems


Workpackages

• WP1: Reliable and secure communication protocols for

wireless automation

• WP2: Communication constrained reliable control

• WP3: Implementation of WiSA toolchain

• WP4: Project management

Aalto KTH


WISA Phase I & II

WISA Phase I WISA Phase II

Tool chain

Control, data fusion and networking algorithms,

testbeds and simulation tools Control and

data fusion

Wireless

networking

Design

tools

WISA Phase I WISA Phase II

Tool chain

Control, data fusion and networking algorithms,

testbeds and simulation tools Control and

data fusion

Wireless

networking

Design

tools

Cro

ss-layer

desig

n


Results: Toolchains

• PiccSIM – Simulation of wireless control systems

• WirelessTools – Planning of wireless network schedule

• PROSE – Node and simulated network


WP 1: Reliable and secure communication

• T1.1. Interference avoidance and dynamic spectrum

management

– Time and frequency domain methods

– Adaptive frequency hopping

• T1.2. Reliable networking

– SIRP, Antenna switching

– Tools for scheduling

• T1.3. Sensor and network monitoring, fault detection,

and fault recovery

– Fault detection part is partly missing

– Fault recovery: Code dissemination tool


WP2: Communication constrained reliable control

• T2.1. Communication-aware data fusion and control

– New data fusion schemes

– Network jitter aware PID tuning rules

• T2.2. Control structures, architectures and scalability

– Impact of MAC on control and data fusion were analyzed

– Tuning of PID controllers for distributed MIMO systems

• T2.3. Adaptive and robust control

– Delay adaptive Internal Model Control based tuning

– Network performance adaptive controller


WP3: Implementation of WiSA Tool Chain

• T3.1. Automated implementation of routing protocols

– This was not accomplished! There is no automation in the

development of routing protocols

– PROSE tool for hardware in the loop simulation

• T3.2. Automated control algorithm implementation

– Part of PiccSIM

• T3.3. Design tools and interfaces for the WiSA tool chain

– Part of PiccSIM

• T3.4. Demonstrator development

– Several demo sessions were arrange (including NORDITE

workshop)


WP 1/T1.1-T1.2: Results

• Objective: wireless sensor nodes should be able to

communicate in a reliable fashion despite bad channel

conditions (interference, fading).

• We aim at improving reliability by means of:

– Interference Avoidance through Dynamic Spectrum Access

– Frequency Hopping

– Channel Coding

RELIABILITY

Dynamic Spectrum Access

Frequency Hopping

Antenna Switching

Receiver diversity

Channel Coding

Hybrid ARQ


WP 1/T1.1: Dynamic Spectrum Access

• An Example: Experimental Comparison of DSA schemes:

Spectrum Holes in the Time domain

Spectrum Holes in the Frequency domain

Performance of DSA in the time domain depends heavily on

channel conditions:

Energy increased

of up to 5 times for

high interference!

DSA in the frequency domain (channel selection) requires larger

energy for spectrum sensing but allows to avoid interference:

By selecting the

communication channel

effects of interference

can be mitigated


WP 1/T1.2: Spatial diversity

-90 -85 -80 -75 -70 -650.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

Mean RSSI (dBm)

Packet

Deliv

ery

Ratio

Time Diversity Approaches for 0.1km/h and 1km/h

Pure Time Diversity (0.1km/h)

Piggybacking (0.1km/h)

Switch if No Acknowledgement (0.1km/h)

Piggybacking (1km/h)

Pure Time Diversity (1km/h)

Switch if No Acknowlodgement (1km/h)

12

Elektrobit’s: Channel Emulator PropSIM-c2

TABLE I

CHANNEL PARAMETERS

Tap CHANNEL 1 CHANNEL 2

Relative

tap delay

[ns]

Relative tap

amplitude

[ns]

Relative tap

delay [ns]

Relative tap

amplitude

[ns]

1 0 0 0 -0.1

2 20 -0.9 20 -0.6

3 30 -2.6 50 -2.9

4 40 -3.5 100 -5.8

5 100 -6.7 150 -8.7

6 300 -17.9 200 -11.6

Multiple receiving antennas:

26% increase in packet delivery ratio


• Antenna switching and receiver selection diversity

WP 1/T1.2: Spatial diversity

1 2 3 4 5 6 7 80

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Links (1-8)

PA

CK

ET

DE

LIV

ER

Y R

AT

IO

Bridge 23.3m, Receiver sensitivity = -94 dBm

Receiver Array

• 10 packets/s

• Dual Antenna System

• Receivers Array (4)


Performance of Multi-Channel MAC Protocols

• Performance of G-McMAC analyzed and compared to other existing

protocols

• G-McMAC outperforms other protocols with respect to delay

regardless of the used parameters

• G-McMAC achieves the highest throughput in many cases.


Time-Synchronization in Multi-Channel WSN

• Multi-Channel Time-Synchronization (MCTS) protocol

• Time-synchronization

– Critical for many WSN applications, e.g. control

– Enables efficient communications and deterministic operation

– Multiple channels can be used simultaneously in order to

minimize convergence time


Control over WirelessHART networks

P

C

WirelessHART network

Data stream characteristics:

• Slotted time

• Minimum transmission delay

• Time-varying latency, loss

Many analysis tools and control design techniques, but no perfect match

– theory most complete for linear-quadratic control

Here: explore sampling interval as ”interface parameter” in co-design.

WP 2/T2.1 communication aware data fusion and control


Realiable real-time challenge

Meeting hard deadlines on unreliable multi-hop network

Maximize deadline-constrained reliability (the “timely

throughput”)

i

i


WISA-II solutions

Focusing on WirelessHART-compliant solutions

New theory, algorithms and software for network

scheduling

– minimize multi-source data collection delay

– maximize deadline-constrained reliability for unicast

joint routing and transmission scheduling

Limits of performance, rules of thumb, and optimal

algorithms


WP 1/T1.2 : Convergecast

Given: sensors with single packet to send at time zero

Find: schedule that delivers all packets to sink (in an optimal fashion)

Key operation WirelessHART’s

sensing-compution-actuation cycle:



Optimal convergecast on trees

Proposition. The minimum evacuation time for a wireless HART

network with tree topology is max(N, 2Nmax-1) timeslots, where

Nmax is the number of nodes in the largest subtree.

Also here, we can characterize the channel-latency tradeoff.

Efficient (O(N2)) time-optimal policies, channel-limited case slightly harder.



If links are unreliable, then the complete operation might fail.

Observation. If links fail with probability pl, convergecast fails with

probability (1-pl)S where S=# transmissions in the schedule.

For line with N nodes, S=N(N+1)/2Schedule quickly becomes unreliable!

Several simple ways of improving reliability of a given schedule

– duplicating each slot, repeating schedule, …

Need methods for quantifying the resulting latency distributions.


Optimal co-design

Understanding what controllers need, and what network can provide

Key result: optimal co-design is modular, can be computed efficiently

deadline-constrained maximum reliability and control under loss

optimal parameters found by sweeping over sampling interval


WirelessHART tools

Key features: • Powerful network editor • Interactive scheduler • Integrated schedule optimizer • Reliability analysis • Matlab/Simulink integration • Multiple superframe support • Sensors, actuators, relays


WP 2/T2.1 Communication aware data fusion and control

• Tune the controller s.t. stable even with varying delay

• One proposed method: Extended plant PID tuning

• Measurement filter design based on the network delays

Filter design

Step experiment Filtering Extended plant response

AMIGO design

on extended plant,

tuning rules

G(s)

1( )

1f n

f

G ssT

max,fT f n max0 ( )t

max

(1 )/ 2

max

1, 1

3

1, 1.

13

nf

n

Tn

nnn


WP 2/T2.3. Adaptive and robust control

• Network congestion causes packet drops

• Adjust control speed and required communication rate

• Maintain good network QoS

– Keep packet drop at 3 %

0 200 400 600 800 1000 12000

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Time [s]

Qo

S

0 200 400 600 800 1000 12000

20

40

Time [s]

0 200 400 600 800 1000 12000

2

4

6

Time [s]

h [s]


WP 3: WISA Toolchains, PiccSIM

• PiccSIM

– Control simulation in Simulink

– Network simulation in ns-2

– Graphical user interfaces for

network design

– Data-based modeling tools,

controller design and tuning

– Automatic code generation, and

code reusability

• All in one tool

• Released as open-source to

researchers

• wsn.tkk.fi/en/software/piccsim

Control design

GUI


WP 3: PiccSIM


SINK

NODES

40M

20M

20M

10M

1 2 30

10

20

30

40

50

60

70

80

90

Mobility Models

Pa

cke

t D

elv

ery

Ra

tio

(%

)

FreeSpace

Light Machinery

Medium Machinery

Heavy Machinery

• A Gilbert-Elliot packet drop model is implemented on PiccSIM

– Each link model consists of the state residence times and the packet

drop probabilities for each state

1 2 3 4 5 6 7 80

10

20

30

40

50

60

Link [#]

Packet

dro

p [

%]

Good Bad0

5000

10000

Good Bad0

5000

10000

Good Bad0

200

400

Good Bad0

100

200

Good Bad0

2000

4000

Good Bad0

100

200

Good Bad0

50

100

Good Bad0

2000

4000

0

0.5

1

0

0.5

1

0

0.5

1

0

0.5

1

0

0.5

1

0

0.5

1

0

0.5

1

0

0.5

1

1 2 3 4 5 6 7 80

10

20

30

40

50

60

70

80

90

100

Link [#]

Packet

dro

p [

%]

Good Bad0

200

400

Good Bad0

1000

2000

Good Bad0

500

1000

Good Bad0

100

200

Good Bad0

1000

2000

Good Bad0

100

200

Good Bad0

2000

4000

Good Bad0

100

200

0

0.5

1

0

0.5

0

0.5

1

0

0.5

1

0

0.5

1

0

0.5

1

0

0.5

1

0

0.5

1

Simulation: Crane Control

WP3: Using Field data in Simulations

Heavy Machinery Light Machinery

Residence time

Packet drop probability


WP3: Automatic Code Generation

• Simulation -> Implementation and testing on real

hardware

• Generic node block in PiccSIM library

– Make implementation in block

• Simulink blocks, Matlab code...

• Radio blocks for communication between nodes

• Matlab Real-Time Workshop

– Target Language Compiler (TLC)

– Generate code from Simulink block

• Wrapper main file for Sensinode node hardware

– Other wrappers can easily be implemented

Synchronize with Ns -2

do { ... } while

Stop bit

Process_interface

AD 0

Radio timestamp 1

Radio recv 1

DA 6

DA 7

Radio send 1

Process

Process

Pendulum _controller

Radio timestamp 1

Radio recv 1

Radio trigger 1

Radio send 1

Node _Controller

Send to N 0 T 3

Send enable

Timestamps

Data N 1 T 2

Node

ID = 2

Node _ KF

Send to N 2 T 2

Timestamps

Data N 0 T 1

Node

ID = 1

Kalman _filter

Radio timestamp 1

Radio recv 1

Radio send 1

Interface node

Send to N 1 T 1

Timestamps

Data N 2 T 3

Node

ID = 0u


WP3: Automatic Code Generation

Radio send 1

3

DA 7

2

DA 6

1

Tapped Delay

4

Delays

Signal Specification

D : 3 Saturation

Rate Transition

Gain 3

2 . 5

Gain 2

2 . 5 / 0 . 8 Gain 1

90 / 2 . 5

Detect

Change

U ~ = U / z

Data Type Conversion

double

Constant

L

Bias 2

u - 2 . 5 / 2

Bias 1

u + 0 . 4

Add

Radio recv 1

3

Radio timestamp 1

2

AD 0

1


PROSE – Hardware in network simulation

• Test hardware with simulated

network

• Wireless protocol

– Testing, debugging

– Logging all activities

– Controllable channel conditions


PROSE communication details


Collaboration

between research groups

• Researcher visits: Lasse Eriksson @ KTH, 5/2006 and 6/2007

• Researcher visits: Mikael Björkbom @ KTH, 5/2009

• One day visits from KTH to Aalto

• Joint publications

between research groups and industry

• Active participation of the industry in the steering board meetings

(e.g. simulation testbed demo attracted Åkerströms (Sweden) to

travel to Helsinki)

• Tomorrow PiccSIM demo at ABB, Sweden

• Joint workshop on ”Standards and research challenges for industrial

wireless control” with industrial partners in Stockholm, Sweden 4th

of March 2008


Information dissemination

• Results summary (2008-2010)

– 5 (+1) Ph.D theses

– 3 Masters theses

– 5 Bachelor theses

– 8 Journals papers

– 38 Conference papers

• Seminar presentations and invited talks:

– DoD/TEKES workshop in Washington 11 - 12 March 2008

– Rutgers/HIIT Workshop on Spontaneous Networks in Rutgers 5-9 May, 2008

– Third International Summer School on Applications of WSN and Wireless

Sensing in the Future Internet (SenZations) in Slovenia 1 - 5 September 2008

– 8th Scandinavian Workshop on Wireless Adhoc Networks (Adhoc' 08) May 7-8,

2008 Johannesberg Estate

– Sensinode research seminar, Vuokatti, Finland, 16th of September 2008

– Lecture on Reliable WSNs at Prairie View Texas A&M, 15th of October 2009

– Presentation at Scandinavian Electronics Event, 14.4.2010


Wireless Sensor Systems group at Aalto

• Started in 2008 to collaborate in the field of WSS

– Made possible by WiSA project

– Aalto University Workshop on Wireless Sensor Systems 2010

• Currently 4 projects, multiple departments, about 15

researchers

• Research fields

– Network Management

– Wireless Automation (Gensen, RELA, RIWA)

– Indoor Situation Awarenes (WISM II)

– Structural Health Monitoring (ISMO)


Final thoughts

• Nordic cooperation

– Closeby, initial visits

– Still videconference more convenient

• NORDITE program

– Nordic cooperation good

– Basic research oriented

• Industry involvement

– Only interest group

– Less feedback than in industrially financed projects


Contact information:

Mikael Björkbom

Aalto University

School of Electrical Engineering

Dept. of Automation and Systems Technology

P.O.Box 15500

FI-00076 AALTO

Finland

Tel. +358 9 470 25213

Email: [email protected]

http://wsn.tkk.fi

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