High performance super-twisting sliding mode control for a maritime autonomous surface ship (MASS) using ADP-Based adaptive gains and time delay estimation - Publikacja - MOST Wiedzy

Wyszukiwarka

High performance super-twisting sliding mode control for a maritime autonomous surface ship (MASS) using ADP-Based adaptive gains and time delay estimation

Abstrakt

This research addresses two kinds of problems related to optimal trajectory tracking of a Maritime Autonomous Surface Ship (MASS): those caused by the time-varying external disturbances including winds, waves and ocean currents as well as those resulting from inherent dynamical uncertainties. As the paper shows, an accurate and robust optimal controller can successfully deal with both issues. An improved Optimal Adaptive Super-Twisting Sliding Mode Control (OAST-SMC) algorithm is proposed here as a robust optimal adaptive strategy. In this strategy, in order to improve performance of the standard super-twisting approach, we apply an Approximate Dynamic Programming (ADP)-based optimal tuning of gains and an underlying concept based on Time Delay Estimation (TDE). An ADP algorithm is implemented using an actor-critic neural network to deal with the curse of dimensionality in Hamilton–Jacobi–Bellman (HJB) equation. The critical role of TDE part in this algorithm is estimating the impact of disturbances and uncertainties on the MASS model. The results have shown that OAST-TDE significantly outperforms the ST-TDE and AST-TDE algorithm in terms of the optimal control efforts. Also, compared with a Nonlinear Model Predictive Control (NMPC), proposed controller meets the optimal control efforts and accurate tracking concurrently.

Cytowania

  • 1 4

    CrossRef

  • 1 5

    Web of Science

  • 1 5

    Scopus

Cytuj jako

Pełna treść

pobierz publikację
pobrano 40 razy
Wersja publikacji
Accepted albo Published Version
Licencja
Creative Commons: CC-BY-NC-ND otwiera się w nowej karcie

Słowa kluczowe

Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
OCEAN ENGINEERING nr 191, strony 1 - 19,
ISSN: 0029-8018
Język:
angielski
Rok wydania:
2019
Opis bibliograficzny:
Nejatbakhsh Esfahani H., Szłapczyński R., Ghaemi M. H.: High performance super-twisting sliding mode control for a maritime autonomous surface ship (MASS) using ADP-Based adaptive gains and time delay estimation// OCEAN ENGINEERING -Vol. 191, (2019), s.1-19
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.oceaneng.2019.106526
Bibliografia: test
  1. Abdelaal, M., Fr, M., Hahn, A., 2018. Nonlinear Model Predictive Control for trajectory tracking and collision avoidance of underactuated vessels with disturbances ☆ 160, 168-180. https://doi.org/10.1016/j.oceaneng.2018.04.026 otwiera się w nowej karcie
  2. Esfahani, H.N., Azimirad, V., Eslami, A., Asadi, S., 2013. An optimal sliding mode control based on immune-wavelet algorithm for underwater robotic manipulator. 2013 21st Iran. Conf. Electr. Eng. ICEE 2013 1-6. https://doi.org/10.1109/IranianCEE.2013.6599587 otwiera się w nowej karcie
  3. Esfahani, H.N., Azimirad, V., Zakeri, M., 2014. SLIDING MODE-PID FUZZY CONTROLLER WITH A NEW REACHING MODE FOR UNDERWATER ROBOTIC MANIPULATORS 258, 2014.
  4. Esfahani, H. N. 2019. Robust Model Predictive Control for Autonomous Underwater Vehicle-Manipulator System with Fuzzy Compensator. Polish Maritime Research (forthcoming), 10.2478/pomr-2019-00139. otwiera się w nowej karcie
  5. Fang, Y., 2004. Global output feedback control of dynamically positioned surface vessels : an adaptive control approach q 14, 341-356. https://doi.org/10.1016/S0957-4158(03)00064-3 otwiera się w nowej karcie
  6. Fossen, T.I., 2016. Handbook of Marine Craft Hydrodynamics and Motion Control [Bookshelf]. IEEE Control Syst. 36, 78-79. https://doi.org/10.1109/mcs.2015.2495095 otwiera się w nowej karcie
  7. Fu, M., Yu, L., 2018. Finite-time extended state observer-based distributed formation control for marine surface vehicles with input saturation and disturbances. Ocean Eng. 159, 219-227. https://doi.org/10.1016/j.oceaneng.2018.04.016 otwiera się w nowej karcie
  8. Huang, H., Gong, M., Zhuang, Y., Sharma, S., Xu, D., 2019. A new guidance law for trajectory tracking of an underactuated unmanned surface vehicle with parameter perturbations. Ocean Eng. 175, 217-222. https://doi.org/10.1016/j.oceaneng.2019.02.042 otwiera się w nowej karcie
  9. Hung, N.T., Rego, F., Crasta, N., Pascoal, A.M., 2018. Input-Constrained Path Following for Autonomous Marine Vehicles with a Global Region of Attraction⁎. IFAC-PapersOnLine 51, 348-353. otwiera się w nowej karcie
  10. https://doi.org/10.1016/j.ifacol.2018.09.499 otwiera się w nowej karcie
  11. Id, D., Rehman, F., Khan, Q., 2018. Smooth super-twisting sliding mode control for the class of underactuated systems 1-21.
  12. Jamalzade, M.S., Koofigar, H.R., Ataei, M., 2016. Adaptive fuzzy control for a class of constrained nonlinear systems with application to a surface vessel. J. Theor. Appl. Mech. 54, 987. https://doi.org/10.15632/jtam-pl.54.3.987 otwiera się w nowej karcie
  13. Kali, Y., Saad, M., Benjelloun, K., 2018. Optimal super-twisting algorithm with time delay estimation for robot manipulators based on feedback linearization. Rob. Auton. Syst. 108, 87-99. https://doi.org/10.1016/j.robot.2018.07.004 otwiera się w nowej karcie
  14. Liu, C., Zheng, H., Negenborn, R.R., Chu, X., 2013. Computational Logistics 8197, 166-180. https://doi.org/10.1007/978-3-642-41019-2 otwiera się w nowej karcie
  15. Liu, C., Zou, Z., Yin, J., 2015. Trajectory tracking of underactuated surface vessels based on neural network and hierarchical sliding mode. J. Mar. Sci. Technol. 20, 322-330. https://doi.org/10.1007/s00773-014-0285-y otwiera się w nowej karcie
  16. Liu, J., Luo, J., Cui, J., Peng, Y., 2016. Trajectory Tracking Control of Underactuated USV with Model Perturbation and External Interference. MATEC Web Conf. 77, 0-5. https://doi.org/10.1051/matecconf/20167709009 otwiera się w nowej karcie
  17. Liu, L., Wang, D., Peng, Z., 2019. State recovery and disturbance estimation of unmanned surface vehicles based on nonlinear extended state observers. Ocean Eng. 171, 625-632. https://doi.org/10.1016/j.oceaneng.2018.11.008 otwiera się w nowej karcie
  18. Liu, L., Wang, D., Peng, Z., Wang, H., 2016. Predictor-based LOS guidance law for path following of underactuated marine surface vehicles with sideslip compensation. Ocean Eng. 124, 340-348. otwiera się w nowej karcie
  19. https://doi.org/10.1016/j.oceaneng.2016.07.057 otwiera się w nowej karcie
  20. Lu, Y., Zhang, G., Sun, Z., Zhang, W., 2018. Robust adaptive formation control of underactuated autonomous surface vessels based on MLP and DOB. Nonlinear Dyn. 94, 503-519. https://doi.org/10.1007/s11071-018-4374-z Nejatbakhsh, H., Azimirad, V., Danesh, M., 2015. A Time Delay Controller included terminal sliding mode and fuzzy gain tuning for Underwater Vehicle-Manipulator Systems. Ocean Eng. 107, 97-107. otwiera się w nowej karcie
  21. https://doi.org/10.1016/j.oceaneng.2015.07.043 otwiera się w nowej karcie
  22. Sharma, S.K., Sutton, R., Motwani, A., Annamalai, A., 2014. Non-linear control algorithms for an unmanned surface vehicle. Proc. Inst. Mech. Eng. Part M J. Eng. Marit. Environ. 228, 146-155. https://doi.org/10.1177/1475090213503630 otwiera się w nowej karcie
  23. Shojaei, K., 2016. Observer-based neural adaptive formation control of autonomous surface vessels with limited torque. Rob. Auton. Syst. 78, 83-96. https://doi.org/10.1016/j.robot.2016.01.005 otwiera się w nowej karcie
  24. Shtessel, Y.B., Moreno, J.A., Plestan, F., Fridman, L.M., Poznyak, A.S., 2010. Super-twisting Adaptive Sliding Mode Control : a Lyapunov Design. pp. 5109-5113. otwiera się w nowej karcie
  25. Sun, Z., Zhang, G., Yi, B., Zhang, W., 2017. Practical proportional integral sliding mode control for underactuated surface ships in the fi elds of marine practice. Ocean Eng. 142, 217-223. https://doi.org/10.1016/j.oceaneng.2017.07.010 otwiera się w nowej karcie
  26. Tanakitkorn, K., Phillips, A.B., Wilson, P.A., Turnock, S.R., 2017. Sliding mode heading control of an overactuated , hover-capable autonomous underwater vehicle with experimental verification 396-415. https://doi.org/10.1002/rob.21766 otwiera się w nowej karcie
  27. Valenciaga, F., 2014. A SECOND ORDER SLIDING MODE PATH FOLLOWING CONTROL FOR AUTONOMOUS SURFACE VESSELS 16, 1515-1521. https://doi.org/10.1002/asjc.840 otwiera się w nowej karcie
  28. Wang, W., Mateos, L.A., Park, S., Leoni, P., Gheneti, B., Duarte, F., Ratti, C., Rus, D., 2018. Design , Modeling , and Nonlinear Model Predictive Tracking Control of a Novel Autonomous Surface Vehicle. IEEE Int. Conf. Robot. Autom. 6189-6196. https://doi.org/10.1109/ICRA.2018.8460632 otwiera się w nowej karcie
  29. Yi, B., Qiao, L., Zhang, W., 2016. Two-time scale path following of underactuated marine surface vessels : Design and stability analysis using singular perturbation methods. Ocean Eng. 124, 287-297. otwiera się w nowej karcie
  30. https://doi.org/10.1016/j.oceaneng.2016.07.006 otwiera się w nowej karcie
  31. Zhang, P., 2018. Dynamic Surface Adaptive Robust Control of Unmanned Marine. J. Robot. 2018. otwiera się w nowej karcie
  32. Zheng, H., Negenborn, R.R., Lodewijks, G., 2014. Trajectory tracking of autonomous vessels using model predictive control. IFAC Proc. Vol. 19, 8812-8818. https://doi.org/10.3182/20140824-6-ZA-1003.00767 otwiera się w nowej karcie
Weryfikacja:
Politechnika Gdańska

wyświetlono 78 razy

Publikacje, które mogą cię zainteresować

Meta Tagi