PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2019 | 26 | 3 |
Tytuł artykułu

Model predictive super-twisting sliding mode control for an utonomous surface vehicle

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a new robust Model Predictive Control (MPC) algorithm for trajectory tracking of an Autonomous Surface Vehicle (ASV) in presence of the time-varying external disturbances including winds, waves and ocean currents as well as dynamical uncertainties. For fulfilling the robustness property, a sliding mode control-based procedure for designing of MPC and a super-twisting term are adopted. The MPC algorithm has been known as an effective approach for the implementation simplicity and its fast dynamic response. The proposed hybrid controller has been implemented in MATLAB / Simulink environment. The results for the combined Model Predictive Super-Twisting Sliding Mode Control (MP-STSMC) algorithm have shown that it significantly outperforms conventional MPC algorithm in terms of the transient response, robustness and steady state response and presents an effective chattering attenuation in comparison with the Super-Twisting Sliding Mode Control (STSMC) algorithm
Słowa kluczowe
EN
Wydawca
-
Rocznik
Tom
26
Numer
3
Opis fizyczny
p.163-171,fig.,ref.
Twórcy
  • Department of Marine Mechatronics, Faculty of Ocean Engineering and Ship Technology, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland
  • Department of Marine Mechatronics, Faculty of Ocean Engineering and Ship Technology, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland
Bibliografia
  • 1. Esfahani, H. N., Azimirad. V., Eslami. A., Asadi. S.): An optimal sliding mode control based on immune-wavelet algorithm for underwater robotic manipulator. Proceedings of the 21st Iranian Conference on Electrical Engineering (ICEE), Mashhad, Iran, 2013.
  • 2. Esfahani, H. N., Azimirad, V., Danesh, M.: A time delay controller included terminal sliding mode and fuzzy gain tuning for underwater vehicle-manipulator systems. Ocean Engineering, Vol. 107, (2015) pp. 97-107.
  • 3. Esfahani, H. N., Azimirad, V., Zakeri, M.: Sliding Mode-PID Fuzzy controller with a new reaching mode for underwater robotic manipulators. Latin American Applied Research, vol. 44(3), (2014), pp. 253–258.
  • 4. Liu C., Zheng H., Negenborn R.R., Chu X., Wang L.: Trajectory tracking control for underactuated surface vessels based on nonlinear Model Predictive Control. In: Corman F., Voß S., Negenborn R. (eds) Computational Logistics. ICCL 2015. Lecture Notes in Computer Science, vol 9335, (2015), pp. 166-180. Springer, Cham. (Proceedings of the 6th International Conference, ICCL 2015, Delft, The Netherlands).
  • 5. Liu, J., Luo, J., Cui, J., Peng, Y.: Tra j e c t o r y Tra c k i n g C o n t r o l of Underactuated USV with Model Perturbation and External Interference. Procedings of the 3rd International Conference on Mechanics and Mechatronics Research (ICMMR 2016). Chongqing, China , 2016. DOI: 10.1051/matecconf/20167709009.
  • 6. Wang, W., Mateos, L.A., Park, S., Leoni, P., Gheneti, B., Duarte, F., Ratti, C., Rus, D.: Design , Modeling , and Nonlinear Model Predictive Tracking Control of a Novel Autonomous Surface Vehicle. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 6189–6196. Brisbane, Australia, 2018. DOI: 10.1109/ICR A.2018.8460632.
  • 7. Zheng, H., Negenborn, R.R., Lodewijks, G.: Tra j e c t o r y tracking of autonomous vessels using model predictive control. IFAC Proceedings Volumes. vol. 19, (2014) no. 3, pp. 8812–8818. (Procedings of the 19th IFAC World Congress, Cape Town, South Africa, August 24-29). DOI: 10.3182/20140824-6-ZA-1003.00767.
  • 8. Abdelaal, M., Fr, M., Hahn, A.: Nonlinear Model Predictive Control for trajectory tracking and collision avoidance of underactuated vessels with disturbances. Ocean Eng., Vol. 160, (2018), pp. 168–180.
  • 9. Yi, B., Qiao, L., Zhang, W.: Two-time scale path following of underactuated marine surface vessels : Design and stability analysis using singular perturbation methods. Ocean Eng., Vol. 124, (2016) , pp. 287–297.
  • 10. Valenciaga, F.: A second order sliding mode path following control for autonomous surface vessels. Asian Journal Control, vol. 16(5), (2014), pp. 1515–1521.
  • 11. Tanakitkorn, K., Phillips, A.B., Wilson, P.A., Turnock, S.R. : Sliding mode heading control of an overactuated hover-capable autonomous underwater vehicle with experimental verification. Journal of Field Robotics, vol. 35(3), (2017), pp. 396–415.
  • 12. Hung, N.T., Rego, F., Crasta, N., Pascoal, A.M.: Input-Constrained Path Following for Autonomous Marine Vehicles with a Global Region of Attraction. IFAC-PapersOnLine, vol. 51(29), pp. 348–353. (Proceedings of the 11th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles, CAMS-2018. Opatija, Croatia, 2018.
  • 13. Jamalzade, M.S., Koofigar, H.R., Ataei, M.: Adaptive fuzzy control for a class of constrained nonlinear systems with application to a surface vessel. Journal of Theoretical and Applied Mechanics, vol. 54(3), (2016), pp. 987-1000.
  • 14 . Fossen, T.I.: Handbook of Marine Craft Hydrodynamics and Motion Control, John Wiley & Sons, Ltd., 2011.
  • 15. Fu, M., Yu, L.: Finite-time extended state observer-based distributed formation control for marine surface vehicles with input saturation and disturbances. Ocean Eng., Vol. 159, (2018) , pp. 219–227.
  • 16. Incremona, G. P., Ferrara, A., Magni, L.: Hierarchical Model Predictive/Sliding Mode Control of Nonlinear Constrained Uncertain Systems. IFAC-PapersOnLine, vol. 48(23), (2015) , pp. 102-109. (Proceedings of the 5th IFAC Conference on Nonlinear Model Predictive Control, NMPC-15. Seville, Spain).
  • 17. Esfahani, H. N: Robust Model Predictive Control for Autonomous Underwater Vehicle–Manipulator System with Fuzzy Compensator. Polish Maritime Research (forthcoming), 2019. 10.2478/pomr-2019-00139.
  • 18. Witkowska, A, Smierzchalski, R.: Adaptive dynamic control allocation for dynamic positioning of marine vessel based on backstepping method and sequential quadratic programming. Ocean Engineering, Vol. 163, (2018) , pp. 570-582.
  • 19. Witkowska, A, Smierzchalski, R.: Adaptive Backstepping Tracking Control for an over–Actuated DP Marine Vessel with Inertia Uncertainties. International Journal of Applied Mathematics and Computer Science , Vol. 28(4), (2018), pp. 679-693.
  • 20. Lisowski, J.: Analysis of Methods of Determining the Safe Ship Trajectory. TRANSNAV-International Journal On Marine Navigation And Safety Of Sea Transportation, Vol. 10(2), (2016) , pp. 223-228.
  • 21. Lisowski, J.: Optimization-supported decision-making in the marine mechatronics systems. Solid State Phenomena, vol. 210, (2014), pp. 215-222.
  • 22. Tomera, M.: Ant colony optimization algorithm applied to ship steering control. Procedia Computer Science, vol. 35, (2014) , pp. 83-92. (Proceedings of the Knowledge-Based and Intelligent Information & Engineering Systems, 18th Annual Conference, KES-2014. Gdynia, Poland).
  • 23. Fang, Y.: Global output feedback control of dynamically positioned surface vessels : an adaptive control approach. Mechatronics, Vol. 14, (2004) , pp. 341–356. DOI: 10.1016/S0957-4158(03)00064-3
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.agro-8f2251db-9aa5-4d4d-a473-5551792a3ebe
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.