Dynamic modeling of a wave glider

  • Chun-lin Zhou
  • Bo-xing Wang
  • Hong-xiang Zhou
  • Jing-lan Li
  • Rong Xiong


We propose a method to establish a dynamic model for a wave glider, a wave-propelled sea surface vehicle that can make use of wave energy to obtain thrust. The vehicle, composed of a surface float and a submerged glider in sea water, is regarded as a two-particle system. Kane’s equations are used to establish the dynamic model. To verify the model, the design of a testing prototype is proposed and pool trials are conducted. The speeds of the vehicle under different sea conditions can be computed using the model, which is verified by pool trials. The optimal structure parameters useful for vehicle designs can also be obtained from the model. We illustrate how to build an analytical dynamics model for the wave glider, which is a crucial basis for the vehicle’s motion control. The dynamics model also provides foundations for an off-line simulation of vehicle performance and the optimization of its mechanical designs.

Key words

Wave-propelled vehicle Dynamic modeling Sea surface vehicle Wave glider 

CLC number



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  1. Caiti, A., Calabró, V., Grammatico, S., et al., 2011. Lagrangian modeling of the underwater wave glider. MTS/IEEE Oceans, p.1–6. https://doi.org/10.1109/Oceans-Spain.2011.6003429Google Scholar
  2. Cameron, S., 1994. Obstacle avoidance and path planning. Ind. Robot, 21(5):9–14. https://doi.org/10.1108/EUM0000000004159CrossRefGoogle Scholar
  3. Carragher, P., Hine, G., Legh-Smith, P., et al., 2013. A new platform for offshore exploration and production. Oilfield Rev., 25(4):40–50.Google Scholar
  4. Cong, B., Cui, H.L., Liu, Z., 2009. Modeling and virtual simulation in random ocean waves. J. Xi’an Technol. Univ., 29(5):475–478 (in Chinese).Google Scholar
  5. Daugherty, R.L., Franzini, J.B., 1997. Fluid Mechanics with Engineering Applications. McGraw-Hill, New York, p.192–198.Google Scholar
  6. Hine, R., Willcox, S., Hine, G., et al., 2009. The wave glider: a wave-powered autonomous marine vehicle. MTS/IEEE Oceans, p.1–6. https://doi.org/10.23919/OCEANS.2009.5422129Google Scholar
  7. Kraus, N., Bingham, B., 2011. Estimation of wave glider dynamics for precise positioning. MTS/IEEE Oceans, p.1–9. https://doi.org/10.23919/OCEANS.2011.6107207Google Scholar
  8. Liu, J.Y., Li, Y.H., Yi, H., et al., 2011. The modeling and analysis of wave powering surface vehicle. MTS/IEEE Oceans, p.1–6. https://doi.org/10.23919/OCEANS.2011.6106971Google Scholar
  9. Lolla, T., Ueckermann, M.P., Yiğit, K., et al., 2012. Path planning in time dependent flow fields using level set methods. IEEE Int. Conf. on Robotics and Automation, p.166–173. https://doi.org/10.1109/ICRA.2012.6225364Google Scholar
  10. Ma, X.F., Xu, X.R., Li, D.G., 1988. A recursive algorithm of robot dynamics based on the Kane’s dynamical equation. J. Beijing Univ. Iron Steel Technol., 10(2):198–208 (in Chinese). https://doi.org/10.13374/j.issn1001-053x.1988.02.030Google Scholar
  11. Manley, J., Hine, G., 2016. Unmanned surface vessels (USVs) as tow platforms: wave glider experience and results. MTS/IEEE Oceans, p.1–5 https://doi.org/10.1109/OCEANS.2016.7761234Google Scholar
  12. Manley, J., Willcox, S., 2010. The wave glider: a new concept for deploying ocean instrumentation. IEEE Instrum. Meas. Mag., 13(6):8–13. https://doi.org/10.1109/MIM.2010.5669607CrossRefGoogle Scholar
  13. Ngo, P., Al-Sabban, W., Thomas, J., et al., 2013. An analysis of regression models for predicting the speed of a wave glider autonomous surface vehicle. Proc. Australasian Conf. on Robotics and Automation, p.1–10.Google Scholar
  14. Ngo, P., Das, J., Ogle, J., et al., 2014. Predicting the speed of a wave glider autonomous surface vehicle from wave model data. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.2250–2256. https://doi.org/10.1109/IROS.2014.6942866Google Scholar
  15. Smith, R.N., Das, J., Hine, G., et al., 2011. Predicting wave glider speed from environmental measurements. MTS/IEEE Oceans, p.1–8. https://doi.org/10.23919/OCEANS.2011.6106989Google Scholar
  16. Song, H., Zhang, J.H., Yang, P., et al., 2016. Modeling of a dynamic dual-input dual-output fast steering mirror system. Front. Inform. Technol. Electron. Eng., in press. https://doi.org/10.1631/FITEE.1601221Google Scholar
  17. Tarn, T.J., Shoults, G.A., Yang, S.P., 1996. A dynamic model of an underwater vehicle with a robotic manipulator using Kane’s method. Auton. Robots, 3(2-3):269–283. https://doi.org/10.1007/BF00141159CrossRefGoogle Scholar
  18. Wiggins, S., Manley, J., Brager, E., et al., 2010. Monitoring marine mammal acoustics using wave glider. MTS/IEEE Oceans, p.1–4. https://doi.org/10.1109/OCEANS.2010.5664537Google Scholar
  19. Zhang, Y.W., Kieft, B., Rueda, C., et al., 2016. Autonomous front tracking by a wave glider. MTS/IEEE Oceans, p.1–4. https://doi.org/10.1109/OCEANS.2016.7761070Google Scholar
  20. Zhou, C.L., Low, K.H., 2014. On-line optimization of biomimetic undulatory swimming by an experimentbased approach. J. Bion. Eng., 11(2):213–225. https://doi.org/10.1016/S1672-6529(14)60042-1CrossRefGoogle Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.College of Control Science and EngineeringZhejiang UniversityHangzhouChina

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