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Robust trajectory tracking control of underactuated surface vehicles with prescribed performance

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, a robust sliding mode tracking controller with prescribed performance is developed for an underactuated surface vehicle (USV) with time-varying external disturbances. Firstly, to guarantee the transient and steadystate performance of the closed-loop system, the error transformation technique is presented. Further, the design of the prescribed performance function implements predefined tracking performance constraints, which eliminate the requirement for prior knowledge about the initial errors. Then, a Lyapunov stability synthesis shows that all closed-loop signals remain bounded and the tracking errors remain strictly within the predefined bounds. Finally, simulations and a comparative study are performed to illustrate the robustness and effectiveness of the proposed robust sliding mode control scheme.
Rocznik
Tom
Strony
148--156
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
autor
  • Dalian Maritime University, LENGHAI RODE, 116026 DALIAN, China
autor
Bibliografia
  • 1. T. Pastore and V. Djapic, “Improving autonomy and control of autonomous surface vehicles in port protection and mine countermeasure scenarios,” Journal of Field Robotics, 2010, 27(6):903–914.
  • 2. Z. Liu, Y. Zhang, X. Yu and Yuan C, “Unmanned surface vehicles: An overview of developments and challenges,” Annual Reviews in Control, 2016, 41:71–93.
  • 3. L. Qiao and W. Zhang, “Adaptive non-singular integral terminal sliding mode tracking control for autonomous underwater vehicles,” IET Control Theory & Applications, 2017, 11(8):1293–1306.
  • 4. W. Dong and Y. Guo, “Global time-varying stabilization of underactuated surface vessel,” IEEE Transactions on Automatic Control, 2005, 50(6):859–864.
  • 5. K. Y. Wichlund, O. J. Sordalen and O. Egeland, “Control properties of underactuated vehicles,” IEEE International Conference on Robotics and Automation, 21–27 May 1995, Nagoya, Japan.
  • 6. E. Lefeber, K. Y. Pettersen and H. Nijmeijer, “Tracking control of an underactuated ship,” IEEE Transactions on Control Systems Technology, 2003, 11(1), 52–61.
  • 7. S. Wang, M. Fu and Y. Wang, “Robust adaptive steering control for unmanned surface vehicle with unknown control direction and input saturation,” International Journal of Adaptive Control and Signal Processing, 2019, 33(2):1214–1224.
  • 8. K. D. Do, Z. P. Jiang and J. Pan, “Robust global stabilization of underactuated ships on a linear course: State and output feedback,” International Journal of Control, 2003, 76(1):1–17.
  • 9. H. N. Esfahani and R. Szlapczynski, “Model predictive super-twisting sliding mode control for an autonomous surface vehicle,” Polish Maritime Research, 2019, 26(3):163–171.
  • 10. P. Liu, H. Yu and S. Cang, “Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances,” Nonlinear Dynamics, 2019, 98:1447–1464.
  • 11. G. Zhang, W. Yan, J. Gao and C. Liu, “High-gain observerbased model predictive control for cross tracking of underactuated autonomous underwater vehicles,” IEEE International Conference on Underwater System Technology: Theory & Applications, 13–14 Dec. 2017, Penang, Malaysia.
  • 12. J. Li, P. M. Lee, B. Jun and Y. K. Lim, “Point-to-point navigation of underactuated ships,” Automatica, 2008, 44(12):3201–3205.
  • 13. C. P. Bechlioulis and G. A. Rovithakis, “Robust adaptive control of feedback linearizable MIMO nonlinear systems with prescribed performance,” IEEE Transactions on Automatic Control, 2008, 53(9):2090–2099.
  • 14. X. Wang, X. Yin and F. Shen, “Disturbance observer based adaptive neural prescribed performance control for a class of uncertain nonlinear systems with unknown backlashlike hysteresis,” Neurocomputing, 2018, 299(19):10–19.
  • 15. C. P. Bechlioulis, Z. Doulgeri and G. A. Rovithakis, “Neuro-adaptive force/position control with prescribed performance and guaranteed contact maintenance,” IEEE Transactions on Neural Networks, 2010, 21(12):1857–1868.
  • 16. O. Elhaki and K. Shojaei, “Neural network-based target tracking control of underactuated autonomous underwater vehicles with a prescribed performance,” Ocean Engineering, 2018, 167(1):239–256.
  • 17. S. He, M. Wang, S. Dai and F. Luo, “Leader-follower formation control of USVs with prescribed performance and collision avoidance,” IEEE Transactions on Industrial Informatics, 2018, 15(1):572–581.
  • 18. T. Gao, J. Huang, Y. Zhou and Y. Song, “Robust adaptive tracking control of an underactuated ship with guaranteed transient performance,” International Journal of Systems Science, 2016, 48(2): 272–279.
  • 19. B. S. Park and S. J. Yoo, “Robust fault-tolerant tracking with predefined performance for underactuated surface vessels,” Ocean Engineering, 2016, 115:159–167.
  • 20. S. J. Yoo and B. S. Park, “Guaranteed performance design for distributed bounded containment control of networked uncertain underactuated surface vessels,” Journal of the Franklin Institute, 2017, 354(3):1584–1602.
  • 21. C. P. Bechlioulis, G. C. Karras, S. Heshmati-Alamdari and K. J. Kyriakopoulos, “Trajectory tracking with prescribed performance for underactuated underwater vehicles under model uncertainties and external disturbances,” IEEE Transactions on Control Systems Technology, 2017, 25(2):429–440.
  • 22. R. Skjetne, Y. I. Fossen and P. V. Kokotovic, “Adaptive maneuvering, with experiments, for a model ship in a marine control laboratory,” Automatica, 2005, 41(2):289–298.
  • 23. Q. Yang and M. Chen, “Adaptive neural prescribed performance tracking control for near space vehicles with input nonlinearity,” Neurocomputing, 2016, 174:780–789.
  • 24. S. Wang, M. Fu, Y. Wang and H. Wei, “Area-keeping robust sliding mode control for underactuated surface vehicle,” Journal of Harbin Engineering University, 2020, 41(5):684–690.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d7724cb8-0d38-498f-8962-bba2c822df7c
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