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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
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