Skip to main content
SpringerLink
Log in

Adaptive Unscented Kalman Filter Based Estimation and Filtering for Dynamic Positioning with Model Uncertainties

  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

A novel adaptive unscented Kalman filter (AUKF) is presented and applied to ship dynamic positioning (DP) system with model uncertainties of time-varying noise statistics, model mismatch and slow varying drift forces. The adaptive algorithm is proposed to simultaneously online adapt the process and measurement noise covariance by adopting the main principle of covariance matching. The measurement noise covariance is adapted based on residual covariance matching method, and then the process noise covariance is adjusted by using adaptive scaling factor. Simulation comparisons among the proposed RQAUKF, the strong tracking UKF (RSTAUKF) and the standard UKF show that the proposed RQAUKF can effectively improve the estimation accuracy and stability, and can assist the controller to obtain better control performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. T. I. Fossen, Handbook of Marine Craft Hydrodynamics and Motion Control, John Wiley & Sons, Chichester, UK, 2011.

    Book  Google Scholar 

  2. T. I. Fossen, and Å. Grøvlen, “Nonlinear output feedback control of dynamically positioned ships using vectorial observer backstepping,” IEEE Transaction on Control Systems Technology, vol. 6, no. 1, pp. 121–128, January 1998.

    Article  Google Scholar 

  3. J. L. Du, X. Hu, H. B. Liu, and C. L. P. Chen, “Adaptive robust output feedback control for a marine dynamic positioning system based on a high-gain observer,” IEEE Transaction on Neural Networks and Learning Systems, vol. 26, no. 11, pp. 2775–2786, November 2015.

    Article  MathSciNet  Google Scholar 

  4. J. G. Balchen, N. A. Jenssen, “Dynamic positioning using kalman filtering and optimal control,” IFAC/IFIP Symposium on Automation on Offshore Oil Field Operation, Holland, Amsterdam, pp. 183–186, 1976.

    Google Scholar 

  5. T. I. Fossen, and T. Perez, “Kalman filtering for positioning and heading control of ships and offshore rigs: estimation the effects of waves, wind, and current,” IEEE Control System, vol. 29, no. 6, pp. 32–46, December 2009.

    Article  MATH  Google Scholar 

  6. A. Alcocer, P. Oliveira, and A. Pascoal, “Study and implementation of an EKF GIB-based underwater positioning system,” Control Engineering Practice, vol. 15, no. 6, pp. 689–701, June 2007.

    Article  Google Scholar 

  7. B. S. Yaakov, X. R. Li, and T. Kirubarajan, Estimation with Application to Tracking and Navigation, Theory Algorithms and Software, Wiley, New York, 2001.

    Google Scholar 

  8. S. J. Julier and J. K. Uhlmann, “A new extension of the Kalman filter to nonlinear systems,” International Symposium on Aerospace/Defence Sensing, Simulations and Controls, Orlando, Florida, USA, 1997.

    Google Scholar 

  9. M. Sepasi and F. Sassani, “On-line fault diagnosis of hydraulic systems using unscented Kalman filter,” International Journal of Control, Automation and Systems, vol. 8, no. 1, pp. 149–156, Feburary 2010.

    Article  Google Scholar 

  10. S. Jafarzadeh, C. Lascu, and M. S. Fadali, “State estimation of induction motor drives using the unscented Kalman filter,” IEEE Transaction on Industrial Electronics, vol. 59, no. 11, pp. 4207–4216, November 2012.

    Article  Google Scholar 

  11. H. G. D. Marina, F. J. Pereda, J. M. Giron-Sierra, and F. Espinosa, “UAV attitude estimation using unscented Kalman filter and TRIAD,” IEEE Transaction on Industrial Electronics, vol. 59, no. 11, pp. 4465–4474, November 2012.

    Article  Google Scholar 

  12. L. Xu, K. Ma, and H. X. Fan, “Unscented Kalman filtering for nonlinear state estimation with correlated noises and missing measurements,” International Journal of Control, Automation and Systems, vol. 16, no. 3, pp. 1011–1020, June 2018.

    Article  Google Scholar 

  13. B. B. Gao, G. G. Hu, S. S. Gao, Y. M. Zhong, and C. F. Gu, “Multi-sensor optimal data fusion for INS/GNSS/CNS integration based on unscented Kalman filter,” International Journal of Control, Automation and Systems, vol. 16, no. 1, pp. 129–140, February 2018.

    Article  Google Scholar 

  14. Å. V. Fannemel, Dynamic Positioning by Nonlinear Model Predictive Control, M. S. thesis, NTNU, pp. 31–48, 2008.

    Google Scholar 

  15. X. F. Wang, Z. J. Zou, Y. Wang, and T. S. Li„ “Path following control of underactuated ships based on unscented Kalman filter,” J. Shanghai Jiaotong Univ. (Sci.), vol. 15, no. 1, pp. 108–113, February 2010.

    Article  MATH  Google Scholar 

  16. Y. Zhao, S. S Gao, J. Zhang, and Q. N Sun, “Robust predictive augmented unscented Kalman filter,” International Journal of Control, Automation and Systems, vol. 12, no. 5, pp. 996–1004, October 2014.

    Article  Google Scholar 

  17. Y. Shi and C. Z. Han, “Adaptive UKF method with applications to target tracking,” ACTA Automatica Sinica, vol. 37, no. 6, pp. 755–759, June 2011.

    MathSciNet  Google Scholar 

  18. M. Q. Zhu, J. J. Hou, Y. Liu, and J. F. Su, “Target locating estimation algorithm based on adaptive scaled unscented Kalman filter,” ACTA Armamentar II, vol. 34, no. 5, pp. 561–566, May 2013.

    Google Scholar 

  19. B. B. Gao, S. S. Gao, Y. M. Zhong, G. G. Hu, and C. F. Gu, “Interacting multiple model estimation-based adaptive robust unscented Kalman filter,” International Journal of Control, Automation and Systems, vol. 15, no. 5, pp. 2013–2025, October 2017.

    Article  Google Scholar 

  20. L. Wang and S. X. Li, “Enhanced multi-sensor data fusion methodology based on multiple model estimation for integrated navigation system,” International Journal of Control, Automation and Systems, vol. 16, no. 1, pp. 295–305, Feburary 2018.

    Article  MathSciNet  Google Scholar 

  21. X. L. Ning, P. P. Huang, J. C. Fang, G. Liu, and S. Z. S. Ge, “An adaptive filter method for spacecraft using gravity assist,” Acta Astronautica, vol. 109, pp. 103–111, April 2015.

    Article  Google Scholar 

  22. H. X. Le and S. Matunaga, “A residual based adaptive unscented Kalman filter for fault recovery in attitude determination system of microsatellites,” Acta Astronautica, vol. 105, no. 1, pp. 30–39, December 2014.

    Article  Google Scholar 

  23. M. Das, S. Sadhu, and T. K. Ghoshal, “An adaptive sigma point filter for nonlinear filtering problems,” International Journal of Electrical, Electronics and Computer Engineering, vol. 2, no. 2, pp. 13–19, December 2013.

    Google Scholar 

  24. A. Rahimi, K. D. Kumar, and H. Alighanbari, “Fault estimation of satellite reaction wheels using covariance based adaptive unscented Kalman filter,” Acta Astronautica, vol. 134, pp. 159–169, May 2017.

    Article  Google Scholar 

  25. Y. Meng, S. S. Gao, Y. M. Zhong, G. G. Hu, and A. Subic, “Covariance matching based adaptive unscented Kalman filter for direct filtering in INS/GNSS integration,” Acta Astronautica, vol. 120, pp. 171–181, December 2016.

    Article  Google Scholar 

  26. L. Li, C. Hua, and H. Yang, “A new adaptive unscented Kalman filter based on covariance matching technique,” Proc. of International Conference on Mechatronics & Control, pp. 1308–1313, 2014.

    Google Scholar 

  27. C. Hajiyev and H. E. Soken, “Robust adaptive unscented Kalman filter for attitude estimation of pico satellites,” Int. J. Adapt. Control Signal Process, vol. 28, no. 2, pp. 107–120, April 2014.

    Article  MathSciNet  MATH  Google Scholar 

  28. W. B. Yang and S. Y. Li, “Autonomous navigation filtering algorithm for spacecraft based on strong tracking UKF,” Systems Engineering and Electronics, vol. 33, no. 11, pp. 2485–2491, November 2011.

    Google Scholar 

  29. L. Cao, D. Qiao, H. Lei, and G. B. Wang, “Strong tracking sigma point predictive variable structure filter for attitude synchronisation estimation,” Journal of Navigation, vol. 71, no. 3, pp. 1–18, January 2018.

    Article  Google Scholar 

  30. G. G. Hu, S. S. Gao, Y. M. Zhong, B. B. Gao, and A. Subic, “Modified strong tracking unscented Kalman filter for nonlinear state estimation with process model uncertainty,” Int. J. Adapt. Control Signal Process, vol. 29, no. 12, pp. 1561–1577, December 2015.

    Article  MathSciNet  MATH  Google Scholar 

  31. J. Feng, X. D. Su, and Y. R. Zhang, “An improved strong tracking UKF based on fading factor,” International Journal of Hybrid Information Technology, vol. 7, no. 4, pp. 1–10, July 2014.

    Article  Google Scholar 

  32. J. L. Du, X. Hu, M. Krstić, and Y. Q. Sun, “Robust dynamic positioning of ships with disturbances under input saturation,” Automatica, vol. 73, pp. 207–214, November 2016.

    Article  MathSciNet  MATH  Google Scholar 

  33. B. S. Park, J. W. Kwon, and H. K Kim, “Neural networkbased output feedback control for reference tracking of underactuated surface vessels,” Automatica, vol. 77, pp. 353–359, March 2017.

    Article  MathSciNet  MATH  Google Scholar 

  34. K. D. Do, “Practical control of underactuated ships,” Ocean Engineering, vol. 37, pp. 1111–1119, September 2010.

    Article  Google Scholar 

  35. S. S. Gao, G. G. Hu, and Y. M. Zhong, “Windowing and random weighting-based adaptive unscented Kalman filter,” Int. J. Adapt. Control Signal Process, vol. 29, no. 2, pp. 201–223, February 2015.

    Article  MathSciNet  MATH  Google Scholar 

  36. J. Wang, “Stochastic modeling for real-time kinematic GPS/GLONASS position,” Navigation, vol. 46, no. 4, pp. 297–305, February 2000.

    Article  Google Scholar 

  37. T. I. Fossen, and T. Perez, Marine systems simulator (MSS) http://www.marinecontrol.org, 2007.

    Google Scholar 

  38. R. Skjetne, The Maneuvering Problem, Ph.D. thesis, NTNU, pp. 123–126, 2005.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Qingdao University of Science and Technology, Qingdao, 266061, China

    Fang Deng, Hua-Lin Yang & Long-Jin Wang

Authors
  1. Fang Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hua-Lin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Long-Jin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Fang Deng or Hua-Lin Yang.

Additional information

Recommended by Associate Editor Andrea Cristofaro under the direction of Editor Myo Taeg Lim. This research was supported by the National Natural Science Foundation of China (Grant No.51609120), the Key Research and Development Program of ShanDong Province (Grant no. 2018GNC112007), and the Project of Shandong Province Higher Educational Science and Technology Program (Grant no. J18KA015).

Fang Deng received the B.S. degree in Process Equipment and control engineering from SiChuan University, China, in 2003. She received the M.S. degree in chemical machinery from ZheJiang University in 2006. She is currently working toward a Ph.D. degree at Qingdao University of Science and Technology. Her current research interests include nonlinear control and estimation, adaptive control, and application in motion control of marine crafts.

Hua-Lin Yang received his B.S. degree in mechanical design and manufacturing from Shandong Institute of Light Industry in 1998, an M.S. degree in mechanical manufacturing and automation from Qingdao University of Science and Technology in 2003, and a Ph.D. degree in chemical machinery from ZheJiang University in 2006. His current research interests include intelligent manufacturing, mechanical design, control and automation.

Long-Jin Wang received his Ph.D. degree in Control Science and Engineering from Harbin Engineering University, Harbin, China, in 2009. From 2009 to 2013, he was an engineer at China Shipbuilding heavy Industry Corporation. Since 2013, he has been a associate professor in Control Science and Engineering at Qingdao University of Science and Technology. His current research interests include model identification and ship motion control.

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, F., Yang, HL. & Wang, LJ. Adaptive Unscented Kalman Filter Based Estimation and Filtering for Dynamic Positioning with Model Uncertainties. Int. J. Control Autom. Syst. 17, 667–678 (2019). https://doi.org/10.1007/s12555-018-9503-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-018-9503-4

Keywords

Search

Navigation