Fault Tolerant ROV Navigation Systembased on Particle Filter

using Hydro-acoustic Position and Doppler Velocity Measurements

Bo Zhao, Ph.D. candidate in CeSOS, NTNUResearch topic: Fault tolerant control for DP

?-2009 M.Eng. in Navigation, Guidance and Control (for aircrafts)

Nov. 2009 Start my Ph.D.

Spring, 2010 Courses, preliminary research

Fall, 2010 Courses, preliminary research

Spring, 2011 Courses in DTU, Denmark. Hooked up with the particle filter

Fall, 2011 Course, research, and papers

Spring, 2012 Research, papers, go to conferences, prepare for experiment

Fall, 2012 Research, papers, go to conferences, do experiment

1. Introduction2. System modeling

3. Fault analysis and modeling

4. Particle filter for fault detection

5. Results

1. Introduction

x

y

z

Length: 144 cm

Width: 82 cm

Height: 80 cm

Net weight: 405 kgPayload: 20 kg

BASIC PARAMETERS x

y

z

Tunnel thruster

2×Main thrusters

2×Vertical thrusters

PROPULSION SYSTEM

Horizontal: 2.0 knot

Vertical: 1.2 knot

Lateral: 1.3 knot

Yaw rate: 60°/s

x

y

z

Camera

Manipulators

Lights

ACCESSORIES x

y

z

DVL (Dopple Velocity Log)depth sensor

HPR (Hydroacoustic position reference)

SENSOR SYSTEM compass

Yaw rate gyro

x

y

z

HPR – Hydro acoustic position reference

Faults: 1. Dropout – when no signal received2. Outlier – Measurement has

significant difference from the true position

DVL – Doppler velocity log

Faults: 1. Dropout – when no signal received2. Bias – small-size constant difference

between the measurement and the true velocity

Navigation: Obtain the position and velocity of the ROV

Disturbance and noise1. System noise2. Model uncertainty3. Measurement noise4. Current 5. Failures

Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss

Navigation: Obtain the position and velocity of the ROV

Disturbance and noise1. System noise2. Model uncertainty3. Measurement noise4. Current 5. Failures

2. System modeling

3. Fault analysis and modeling

4. Particle filter for fault detection

5. Results

1. Introduction

Kinematics:

Kinetics:

Current:

DVL:

HPR:

Pictures from http://www.gris.uni-tuebingen.de/people/staff/sfleck/smartsurv3d/http://perception.inrialpes.fr/~chari/myweb/Research/http://wires.wiley.com/WileyCDA/WiresArticle/articles.html?doi=10.1002%2Fwics.1210

Particle filter

Observer for ROV :

3. Fault analysis and modeling

4. Particle filter for fault detection

5. Results

2. System modeling1. Introduction

Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss

Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss

HPR data

HPR update interval

Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss

HPR data

HPR update interval

Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss

5400 5600 5800 6000 6200-35

-30

-25

-20

-15

-10

-5

0

Time [sec]

Eas

t ve

loci

ty [m

/sec

]

DVL data

Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss

Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss

𝝉𝐭𝐡𝐫={𝝉𝐜𝐦𝐝 ,𝝉𝐜𝐦𝐝<𝝉𝟎

𝝉𝟎 ,𝝉𝐜𝐦𝐝≥𝝉𝟎

Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss

54005600

58006000

6200

-35

-30

-25

-20

-15

-10

-5

0

Time [sec]

Eas

t ve

loci

ty [m

/sec

]

Comment:0. If the fault in the system is known, we can design an filter to solve the observation problem1. It is not easy to design observers for the

system models in different failure modes2. Even if a bank of observers is designed, it is

hard to decide which one to use, since the failure mode is unknown.

Comment:0. If the fault in the system is known, we can design an filter to solve the observation problem

4. Particle filter for fault detection

5. Results

3. Fault analysis and modeling

2. System modeling1. Introduction

How do we cognize the world?

Observation

CorrectionPrediction

How do we diagnose a fault?

Fault Free

Faulty

PredictedFault free behavior

PredictedFaulty behavior

Prediction

How do we diagnose a fault?

Fault Free

Faulty

PredictedFault free behavior

PredictedFaulty behavior

Prediction

How do we diagnose a fault?

Fault Free

Faulty

PredictedFault free behavior

PredictedFaulty behavior

Prediction ObservationTake the measurement

Correction

H1

H2

Obs

Compare

Introduction to Particle FilterOutline • Example - Measurement noise

System States

State Estimation

Kalman Filter

Particle Filter

Case Study

p

USED

SLIDES

Introduction to Particle FilterOutline • Example - Measurement noise

System States

State Estimation

Kalman Filter

Particle Filter

Case Study

mp

p

Measuring

USED

SLIDES

Introduction to Particle FilterOutline • Example - Measurement noise

System States

State Estimation

Kalman Filter

Particle Filter

Case Study

mp

Estimating

USED

SLIDES

Introduction to Particle FilterOutline • Example - Measurement noise

System States

State Estimation

Kalman Filter

Particle Filter

Case Study

p

mp

Estimating

USED

SLIDES

p

mp

H1

H2

ObsCorrection

How do we diagnose a fault?

Fault Free

Faulty

PredictedFault free behavior

PredictedFaulty behavior

Prediction ObservationTake the measurement

Correction

H1

H2

Obs

Compare

5. Results

4. Particle filter for fault detection

3. Fault analysis and modeling

2. System modeling1. Introduction

What has been talked about?• ROV, and its navigation sensors• Faults in the sensors and their model• The concept of fault detection with

particle filter• Simulation results

What are the advantages?• Straight-forward modeling• Do the navigation and fault

handling with in a single structure• Extendable