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In this article, a modified L1-adaptive controller with auto-tuning using a genetic algorithm is presented for dynamic positioning of remotely operated vehicles (ROVs) under marine currents, based on a six-degree-of-freedom Nonlinear model of an ROV. To enable tuning of some of the parameters of the controller, a cost function related to the error of the steady state positions of the system is minimised with the use of the genetic algorithm. A series of simulations are conducted to ascertain the performance of the system with the implemented controller, taking into consideration the vehicle position, orientation, and control signals sent as commands to the thrusters. The simulations are carried out with noise levels representative of those encountered by the standard underwater instrumentation on an ROV, as well as with underwater current velocities. In addition, the results are compared with those of a classical controller to verify the improvements offered by the controller proposed in this paper.
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Tom
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115--123
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Bibliogr. 21 poz., rys., tab.
Twórcy
autor
- Departamento de Tecnología Electrónica, Ingeniería de Sistemas y Automática, Universidad de Cantabria, Santander, Spain
autor
- School of Energy and Electronic Engineering, University of Portsmouth, Portsmouth, United Kingdom
autor
- Departamento de Tecnología Electrónica, Ingeniería de Sistemas y Automática, Universidad de Cantabria, Santander, Spain
autor
- Departamento de Tecnología Electrónica, Ingeniería de Sistemas y Automática, Universidad de Cantabria, Santander, Spain
autor
- Departamento de Tecnología Electrónica, Ingeniería de Sistemas y Automática, Universidad de Cantabria, Santander, Spain
Bibliografia
- 1. Amundsen HB, Caharija W, Pettersen KY. Autonomous ROV inspections of aquaculture net pens using DVL. IEEE Journal of Oceanic Engineering 2022, vol. 47, pp. 1-19. https://doi.org/10.1109/JOE.2021.3105285.
- 2. Zhao C, Thies PR, Johanning L. Offshore inspection mission modelling for an ASV/ROV system. Ocean Engineering 2022, vol. 259, p. 111899. https://doi.org/10.1016/j.oceaneng.2022.111899.
- 3. Fay D, Stanton N, Roberts APJ. Exploring ecological interface design for future ROV capabilities in Maritime command and control. In: Stanton, N. (ed.) Advances in Human Aspects of Transportation. AHFE 2018. Advances in Intelligent Systems and Computing, vol. 786. Springer, Cham. http://dx.doi.org/10.1007/978-3-319-93885-1_24.
- 4. Soylu S, Proctor AA, Podhorodeski RP, Bradley C, Buckham BJ. Precise trajectory control for an inspection class ROV. Ocean Engineering 2016 vol. 111, pp. 508-523. https://doi.org/10.1016/j.oceaneng.2015.08.061.
- 5. Fossen TI. Handbook of marine craft hydrodynamics and motion control. John Wiley and Sons, Ltd; 2011. https://doi.org/10.1002/9781119994138.
- 6. Fossen TI. Marine control systems: Guidance, navigation and control of ships, rigs and underwater vehicles. Marine Cybernetics; 2002.
- 7. Cao Y, Li B, Li Q, Stokes AA, Ingram DM, Kiprakis A. A nonlinear model predictive controller for remotely operated underwater vehicles with disturbance rejection. IEEE Access 2020, vol. 8, pp. 158622-158634. https://doi.org/10.1109/ACCESS.2020.3020530.
- 8. Zheng H, Wu J, Wu W, Zhang Y. Robust dynamic positioning of autonomous surface vessels with tube-based model predictive control. Ocean Engineering 2020, vol. 199, p. 106820. https://doi.org/10.1016/j.oceaneng.2019.106820.
- 9. Sainz JJ, Revestido Herrero E, Llata JR, Gonzalez-Sarabia E, Velasco FJ, Rodriguez-Luis A, Fernandez-Ruano S, Guanche R. LQG control for dynamic positioning of floating caissons based on the Kalman filter. Sensors 2021, vol. 21. pp. 1-18. https://doi.org/10.3390/s21196496.
- 10. Cao C, Hovakimyan N. L1 adaptive controller for systems with unknown time-varying parameters and disturbances in the presence of non-zero trajectory initialization error. International Journal of Control 2008, vol. 81, pp. 1147-1161. https://doi.org/10.1080/00207170701670939.
- 11. Vajpayee V, Becerra V, Bausch N, Deng J, Shimjith SR, Arul AJ. L1-adaptive robust control design for a pressurized water-type nuclear power plant. IEEE Transactions on Nuclear Science 2021, vol. 68, pp. 1381-1398. https://doi.org/10.1109/TNS.2021.3090526.
- 12. Maalouf D, Creuze V, Chemori A. A novel application of multivariable L1 adaptive control: From design to real-time implementation on an underwater vehicle. Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), 7-12 October 2012. Vilamoura-Algarve, Portugal. DOI: https://doi.org/10.1109/IROS.2012.6385498.
- 13. Sainz JJ, Becerra V, Revestido Herrero E, Llata JR and Velasco FJ. L1 adaptive control for marine structures. Mathematics (Basel) 2023, vol. 11, p. 3554. https://doi.org/10.3390/math11163554.
- 14. Goheen KR, Jefferys ER. Multivariable self-tuning autopilots for autonomous and remotely operated underwater vehicles. IEEE Journal of Oceanic Engineering 1990, vol. 15, pp. 144-151. https://doi.org/10.1109/48.107142.
- 15. Tanveer A, Ahmad SM. High fidelity modelling and GA optimized control of yaw dynamics of a custom built remotely operated unmanned underwater vehicle. Ocean Engineering 2022, vol. 266, p. 112836. https://doi.org/10.1016/j.oceaneng.2022.112836.
- 16. Von Benzon M, Sřrensen FF, Uth E, Jouffroy J, Liniger J, Pedersen S. An open-source benchmark simulator: Control of a BlueROV2 underwater robot. Journal of Marine Science and Engineering 2022, vol. 10, p. 1898. https://doi.org/10.3390/jmse10121898.
- 17. Ng P, Krieg M. Modifications to ArduSub that improve BlueROV SITL accuracy and design of hybrid autopilot. Applied Sciences 2024, vol. 14, no. 17, p. 7453. https://doi.org/10.3390/app14177453.
- 18. Hovakimyan N, Cao C. L1 adaptive control theory: Guaranteed robustness with fast adaptation. Society for Industrial and Applied Mathematics 2010. https://doi.org/10.1137/1.9780898719376.
- 19. Fossen TI, Perez T. Kalman filtering for positioning and heading control of ships and offshore rigs. IEEE Control Systems Magazine 2009, vol. 29, pp. 32-46. https://doi.org/10.1109/MCS.2009.934408.
- 20. Pomet J, Praly L. Adaptive nonlinear regulation: Estimation from the Lyapunov equation. IEEE Transactions on Automatic Control 1992, vol. 37, pp. 729-740. https://doi.org/10.1109/9.256328.
- 21. Goldberg DE. Genetic algorithms in search, optimization, and machine learning. Addison-Wesley; 1989.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-7c90a8a4-aca6-47ca-b3cf-8762362bc14d
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