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Fault Tolerant ROV Navigation System        based on Particle Filter    using Hydro-acoustic Position and Doppler Velocity...
Bo Zhao, Ph.D. candidate in CeSOS, NTNU        Research topic: Fault tolerant control for DP     ?-2009 M.Eng. in Navigati...
2. System modeling1. Introduction                    3. Fault analysis and                          modeling   5. Results ...
yx        z
y                                                x                                                          z             ...
y                                        x                                                   z2×Vertical thrusters        ...
y                                 x                                         z               Lights                        ...
y                           x               compass           Yaw rate gyro                z                              ...
HPR– Hydro acoustic position reference                                      Faults:                                      1...
DVL– Doppler velocity log                         Faults:                         1. Dropout – when no signal received    ...
Navigation: Obtain the position and velocity of the ROVDisturbance and noise1. System noise2. Model uncertainty3. Measurem...
Navigation: Obtain the position and velocity of the ROVDisturbance and noise               Failure modes1. System noise   ...
2. System modeling1. Introduction                    3. Fault analysis and                          modeling   5. Results ...
Observer for ROV :Particle filter Pictures from http://www.gris.uni-tuebingen.de/people/staff/sfleck/smartsurv3d/ http://p...
2. System modeling1. Introduction                    3. Fault analysis and                          modeling   5. Results ...
Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss
HPR data        HPR update intervalFailure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss
HPR data        HPR update intervalFailure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss
0                                                       -5                                                      -10       ...
Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss
Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss
Comment:                                                  0                                                               ...
2. System modeling1. Introduction                    3. Fault analysis and                          modeling   5. Results ...
How do we   cognize the world?                 Observation    Prediction                 Correction
How do we   diagnose a fault?                        Prediction                                     Predicted             ...
How do we   diagnose a fault?                        Prediction                                     Predicted             ...
How do we    diagnose a fault?Prediction                          Observation              Predicted                 Take ...
Introduction to Particle Filter    Outline System StatesState Estimation Kalman Filter Particle Filter  Case Study        ...
Introduction to Particle Filter    Outline System StatesState Estimation Kalman Filter              Measuring Particle Fil...
Introduction to Particle Filter    Outline System StatesState Estimation Kalman Filter           Estimating Particle Filte...
Introduction to Particle Filter    Outline System StatesState Estimation Kalman Filter               Estimating Particle F...
Correction        Obs   H1   H2                        p             pm
How do we    diagnose a fault?Prediction                          Observation              Predicted                 Take ...
2. System modeling1. Introduction                    3. Fault analysis and                          modeling   5. Results ...
What has been talked about?• ROV, and its navigation sensors• Faults in the sensors and their model• The concept of fault ...
Fault Tolerant ROV Navigation System based on Particle Filter using Hydro-acoustic Position and Doppler Velocity Measureme...
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Fault Tolerant ROV Navigation System based on Particle Filter using Hydro-acoustic Position and Doppler Velocity Measurements

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Transcript of "Fault Tolerant ROV Navigation System based on Particle Filter using Hydro-acoustic Position and Doppler Velocity Measurements "

  1. 1. Fault Tolerant ROV Navigation System based on Particle Filter using Hydro-acoustic Position and Doppler Velocity Measurements
  2. 2. Bo Zhao, Ph.D. candidate in CeSOS, NTNU Research 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 researchSpring, 2011 Courses in DTU, Denmark. Hooked up with the particle filter Fall, 2011 Course, research, and papersSpring, 2012 Research, papers, go to conferences, prepare for experiment Fall, 2012 Research, papers, go to conferences, do experiment
  3. 3. 2. System modeling1. Introduction 3. Fault analysis and modeling 5. Results 4. Particle filter for fault detection
  4. 4. yx z
  5. 5. y x z Width: 82 cm Height: 80 cm Length: 144 cmNet weight: 405 kg Payload: 20 kg
  6. 6. y x z2×Vertical thrusters Vertical: 1.2 knot 2×Main thrusters Tunnel thruster Yaw rate: 60°/s
  7. 7. y x z Lights CameraManipulators
  8. 8. y x compass Yaw rate gyro z HPR (Hydroacoustic position reference) DVL (Dopple Velocity Log)depth sensor
  9. 9. HPR– Hydro acoustic position reference Faults: 1. Dropout – when no signal received 2. Outlier – Measurement has significant difference from the true position
  10. 10. DVL– Doppler velocity log Faults: 1. Dropout – when no signal received 2. Bias – small-size constant difference between the measurement and the true velocity
  11. 11. Navigation: Obtain the position and velocity of the ROVDisturbance and noise1. System noise2. Model uncertainty3. Measurement noise4. Current5. Failures
  12. 12. Navigation: Obtain the position and velocity of the ROVDisturbance and noise Failure modes1. System noise 1. HPR dropout2. Model uncertainty 2. HPR outlier3. Measurement noise 3. DVL dropout4. Current 4. DVL bias5. Failures 5. Thruster loss
  13. 13. 2. System modeling1. Introduction 3. Fault analysis and modeling 5. Results 4. Particle filter for fault detection
  14. 14. Observer for ROV :Particle filter 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
  15. 15. 2. System modeling1. Introduction 3. Fault analysis and modeling 5. Results 4. Particle filter for fault detection
  16. 16. Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss
  17. 17. HPR data HPR update intervalFailure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss
  18. 18. HPR data HPR update intervalFailure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss
  19. 19. 0 -5 -10 East velocity [m/sec] DVL data -15 -20 -25 -30 -35 5400 5600 5800 6000 6200 Time [sec]Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss
  20. 20. Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss
  21. 21. Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss
  22. 22. Comment: 0 -5 0. If the fault in the system is known, we can -10 m/sec] design an filter to solve the observation problem -15 locity [ 1. It is not easy to design observers for the -20 East ve system models in different failure modes-25 2. Even if a bank of observers is designed, it is -30 hard to decide which one to use, since the -35 5400 failure mode is unknown. 5600 5800 Tim e 6000 [se c] 6200Failure modes1. HPR dropout2. HPR outlier3. DVL dropout4. DVL bias5. Thruster loss
  23. 23. 2. System modeling1. Introduction 3. Fault analysis and modeling 5. Results 4. Particle filter for fault detection
  24. 24. How do we cognize the world? Observation Prediction Correction
  25. 25. How do we diagnose a fault? Prediction Predicted Fault free behavior Predicted Faulty behavior
  26. 26. How do we diagnose a fault? Prediction Predicted Fault free behavior Predicted Faulty behavior
  27. 27. How do we diagnose a fault?Prediction Observation Predicted Take the measurement Fault free behavior Correction Obs H1 Predicted Compare Faulty behavior H2
  28. 28. Introduction to Particle Filter Outline System StatesState Estimation Kalman Filter Particle Filter Case Study p
  29. 29. Introduction to Particle Filter Outline System StatesState Estimation Kalman Filter Measuring Particle Filter pm Case Study p
  30. 30. Introduction to Particle Filter Outline System StatesState Estimation Kalman Filter Estimating Particle Filter Case Study pm
  31. 31. Introduction to Particle Filter Outline System StatesState Estimation Kalman Filter Estimating Particle Filter p Case Study pm
  32. 32. Correction Obs H1 H2 p pm
  33. 33. How do we diagnose a fault?Prediction Observation Predicted Take the measurement Fault free behavior Correction Obs H1 Predicted Compare Faulty behavior H2
  34. 34. 2. System modeling1. Introduction 3. Fault analysis and modeling 5. Results 4. Particle filter for fault detection
  35. 35. 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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