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1 Challenge the future Ship motion compensation platform for high payloads dynamic analysis and control MSc Project at GustoMSC – Wouter de Zeeuw Prof.dr.

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Presentation on theme: "1 Challenge the future Ship motion compensation platform for high payloads dynamic analysis and control MSc Project at GustoMSC – Wouter de Zeeuw Prof.dr."— Presentation transcript:

1 1 Challenge the future Ship motion compensation platform for high payloads dynamic analysis and control MSc Project at GustoMSC – Wouter de Zeeuw Prof.dr. D.J. Rixen, Ir. M. Wondergem

2 2 Challenge the future Introduction: Pooltable on cruise ship

3 3 Challenge the future Two ship motion compensation platforms: Ampelmann (personnel) Bargemaster (~400 tonnes)

4 4 Challenge the future Feed the components to the site Jack up and install Offshore windturbine installation with Jack-up units Present method

5 5 Challenge the future Goal: Complete windturbine installation from a floating unit 1000[t] 400[t] (concept of competitor) Motion stabilizing platform to extend operating limits Fast feeder barges Jack up unit stays at site

6 6 Challenge the future Small overview Preliminary 2D model 1.Analysis of Ampelmann scalemodel tests 2.3D modeling of new mechanism on ship 3.Controlling the system

7 7 Challenge the future Goal: Complete windturbine installation from a floating unit Preliminary 2D model showed feasibility... 400[t] (concept of competitor)

8 8 Challenge the future Goal: Complete windturbine installation from a floating unit Preliminary 2D model showed feasibility but dynamic instability 400[t] (concept of competitor)

9 9 Challenge the future 1. (In-)stability due to the quasistatic control? Similar mass system: Ampelmann scale model tests

10 10 Challenge the future Maximum real part of eigenvalues of system matrix. 1 parameter varied around maximum likelihood estim. 1. Stability of the fitted linear model All parameters as in fit of first 15 seconds e.g. c H times 2 (double the hydrodyn. damping) others identical UNSTABLE

11 11 Challenge the future 1. Stability of the fitted linear model Maximum real part of eigenvalues of system matrix. 1 parameter varied around maximum likelihood estim. Adding hydro-damping stabilizes

12 12 Challenge the future 1. Stability of the fitted linear model Maximum real part of eigenvalues of system matrix. 1 parameter varied around maximum likelihood estim. The damping on opposite movements is a destabilizing factor, possible unmodeled nonlinearities

13 13 Challenge the future 1. Stability of the fitted linear model Maximum real part of eigenvalues of system matrix. 1 parameter varied around maximum likelihood estim. The proportional control has stable and unstable settings

14 14 Challenge the future 2. 3D modeling - ship movements Accelerations due to planar movements surge, sway and yaw are smaller than due to off planar movements Platform should compensate heave, roll and pitch

15 15 Challenge the future 2. 3D modeling - platform mechanism New mechanism for a 3 degree of freedom platform Planar movements are constrained by 3 Sarrus type linkages Force vs. Reach variable via α

16 16 Challenge the future 2. 3D modeling - hydrodynamics Barge panel model (35x115m) State-Space approx. of wave radiation terms External wave field realization

17 17 Challenge the future 2. 3D modeling - vessel+platform Lagrangian dynamics (body fixed) Extension of serial robot on ship to parallel robots

18 18 Challenge the future 2. 3D modeling - total dynamics Nonlinear kinematics Coriolis terms Pose dep. mass matrix External waveloads Hydrostatics Hydrodynamics Wave radiation Added mass/damping

19 19 Challenge the future 2. 3D modeling - total dynamics

20 20 Challenge the future 3. Controllers - Naïve Quasistatic vs. Model Based Quasistatic: Calculate leg length error assuming fixed boat position 2 Proportional-Integral-Derivative (PID) controllers On mean error On asymmetric errors Model Based: Nonlinear Model Predictive Control

21 21 Challenge the future 3. Control - Nonlinear Model Predictive Control

22 22 Challenge the future 3. Control - Nonlinear Model Predictive Control

23 23 Challenge the future Visualizations

24 24 Challenge the future Visualizations

25 25 Challenge the future Heave-Roll-Pitch in storm conditions Head sea, seastate Hs=4m T1=6.5s.

26 26 Challenge the future Energy usage in disturbance rejection Milder sea

27 27 Challenge the future Conclusions The scalemodel roll instability can be reproduced by a linear model with quasistatic control and influential parameters can be recognized. The coupled ship - parallel platform dynamics are derived and he new platform can compensate the ship movements. MPC is shown to be a successful candidate for control, requires less power than PID in disturbance rejection and is less hard to tune and to stabilize.

28 28 Challenge the future

29 29 Challenge the future Thank you. Questions?

30 30 Challenge the future #1: Second degree model fit, technique Fitting technique:

31 31 Challenge the future #1b: Second degree model fit, results

32 32 Challenge the future #2: Kinematics – leg joint velocity Jacobian construction

33 33 Challenge the future #3: Hydrodynamics – rad forces Retardation forces (vector): Cummins eqn. in hydrodyn. ref. frame: State space approx. per radiation component (scalar): Known values via hydrodynamic code (WAMIT): Approx. model: (Gauss-Newton iter)

34 34 Challenge the future #4a: Dynamics – Pose dep. mass matrix (Relative velocity) (ref. frame transf.) (notation) (expand) The total mass matrix is now (platform) pose dependent

35 35 Challenge the future #4b: Dynamics - Lagrange (euler angle rates) (body fixed general velocities)


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