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Robust model predictive control for autonomous Underwater Vehicle – Manipulator System with fuzzy compensator

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Języki publikacji
EN
Abstrakty
EN
This paper proposes an improved Model Predictive Control (MPC) approach including a fuzzy compensator in order to track desired trajectories of autonomous Underwater Vehicle Manipulator Systems (UVMS). The tracking performance can be affected by robot dynamical model uncertainties and applied external disturbances. Nevertheless, the MPC as a known proficient nonlinear control approach should be improved by the uncertainty estimator and disturbance compensator particularly in high nonlinear circumstances such as underwater environment in which operation of the UVMS is extremely impressed by added nonlinear terms to its model. In this research, a new methodology is proposed to promote robustness virtue of MPC that is done by designing a fuzzy compensator based on the uncertainty and disturbance estimation in order to reduce or even omit undesired effects of these perturbations. The proposed control design is compared with conventional MPC control approach to confirm the superiority of the proposed approach in terms of robustness against uncertainties, guaranteed stability and precision.
Słowa kluczowe
Rocznik
Tom
Strony
104--114
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
  • Gdansk University of Technology ul. G. Narutowicza 11/12 , 80-233 Gdansk Poland
Bibliografia
  • 1. Islam S., Liu J.: Robust Sliding Mode Control for Robot Manipulators. IEEE Transactions on Industrial Electronics, vol. 58(6), pp. 2444-2453, 2011.
  • 2. F. Piltan and B. Sulaiman.: Review of Sliding Mode Control of Robotic Manipulator. World Sciences Journal, vol. 18(12), pp. 1855-1869, 2012.
  • 3. Esfahani H. N., Azimirad V., Eslami A., Asadi S.: An optimal sliding mode control based on immunewavelet algorithm for underwater robotic manipulator. Proceedings of the 21st Iranian Conference on Electrical Engineering (ICEE). Mashhad, Iran, 2013. DOI: 10.1109/ IranianCEE.2013.6599587.
  • 4. 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, pp. 97-107, 2015.
  • 5. 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), pp. 253–258, 2014.
  • 6. Vivas A., Mosquera V.: Predictive functional control of a PUMA robot. Proceedings of the Conference on Automatic Control and System Engineering (ACSE 05), CICC. Cairo, Egypt, 2005.
  • 7. Incremona G. P., Ferrara A., Magni L.: Hierarchical Model Predictive/Sliding Mode Control of Nonlinear Constrained Uncertain Systems. Proceedings of the 5th IFAC Conference on Nonlinear Model Predictive Control (NMPC). Seville, Spain, 2015. DOI: 10.1016/j.ifacol.2015.11.268.
  • 8. Ghazaei Ardakani M., Olofsson B., Robertsson A., Johansson R.: Real-Time Trajectory Generation using Model Predictive Control. Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE). Gothenburg, Sweden. pp. 942 – 948, 2015. DOI: 10.1109/CoASE.2015.7294220.
  • 9. Wang Y., Chen W., Tomizuka M., Alsuwaidan B. N.: Model predictive sliding mode control: for constraint satisfaction and robustness. ASME 2013 Dynamic Systems and Control Conference, Palo Alto, California, USA. vol. 3, ISBN 978- 0-7918-5614-7, 2013.
  • 10. Fossen. T. I.: Marine Control Systems: Guidance, Navigation and Control of Ships, Rigs and Underwater Vehicle. Marine Cybernetics AS, Norway. pp. 570-579, ISBN 82-92356-00- 2, 2002.
  • 11. Krid. M, Benamar. F, and Lenain. R.: A new explicit dynamic path tracking controller using Generalized Predictive Control. International Journal of Control, Automation and Systems, Springer, vol. 15, Issue 1, pp. 303-314, 2017.
  • 12. Spong M.W., Hutchinson S., Vidyasagar M.: Robot Dynamics and Control. 2nd ed. John Wiley & Sons, Inc.; Hoboken, NJ, USA: 2004.
  • 13. Jasour A. M., Farrokhi M.: Path Tracking and Obstacle Avoidance for Redundant Robotic Arms Using Fuzzy NMPC. Proceedings of the American Control Conference, pp. 1353-1358. St. Louis, MO, USA, 2009. DOI: 10.1109/ ACC.2009.5160408.
  • 14. Rubus. T, Seweryn. K, and Sasiadek. J. Z.: Application of predictive control for manipulator mounted on a satellite. Archives of Control Sciences, vol. 28(LXIV), pp. 105–118, 2018.
  • 15. 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), pp. 223-228, JUN 2016.
  • 16. Lisowski. J.: Optimization-supported decision-making in the marine mechatronics systems. mechanics and material II. Book series: solid state phenomena, Vol. 210, pp. 215- 222, 2014.
  • 17. Tomera. M.: Ant colony optimization algorithm applied to ship steering control. KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 18TH ANNUAL CONFERENCE, KES-2014 Book Series: Procedia Computer Science, Vol. 35, pp. 83-92, 2014.
  • 18. Sun, Y. C., Cheah, C. C.: Adaptive set point control for autonomous underwater vehicles. In: Proceedings of IEEE Robotics Decision and Control Conference, Hawaii USA, 2, pp. 1262–1267,2003.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2da6b8c1-5c0b-4bad-a8b7-8341ab0bbcdd
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