Model predictive ship trajectory tracking system based on line of sight method

• Autorzy: Miller Anna

Identyfikatory

DOI

10.24425/bpasts.2023.145763

• Języki publikacji: EN

Abstrakty

EN

Maritime Autonomous Surface Ships (MASS) perfectly fit into the future vision of merchant fleet. MASS autonomous navigation system combines automatic trajectory tracking and supervisor safe trajectory generation subsystems. Automatic trajectory tracking method, using line-of-sight (LOS) reference course generation algorithm, is combined with model predictive control (MPC). Algorithm for MASS trajectory tracking, including cooperation with the dynamic system of safe trajectory generation is described. It allows for better ship control with steady state cross-track error limitation to the ship hull breadth and limited overshoot after turns. In real MASS ships path is defined as set of straight line segments, so transition between trajectory sections when passing waypoint is unavoidable. In the proposed control algorithm LOS trajectory reference course is mapped to the rotational speed reference value, which is dynamically constrained in MPC controller due to dynamically changing reference trajectory in real MASS system. Also maneuver path advance dependent on the path tangential angle difference, to ensure trajectory tracking for turns from 0 to 90 degrees, without overshoot is used. All results were obtained with the use of training ship in real–time conditions.

Słowa kluczowe

EN

model predictive control; MPC; ship control; autonomous ship; trajectory tracking; line of sight; LOS

PL

kontrola statku; statek autonomiczny; śledzenie trajektorii; linia wzroku; LOS; kontrola predykcyjna modelu; MPC

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2023
• Tom: Vol. 71, nr 5
• Strony: art. no. e145763
• Opis fizyczny: Bibliogr. 33 poz., rys., tab.

Twórcy

autor

Miller Anna

• Gdynia Maritime University, ul. Morska 81-87, 81-225 Gdynia, Poland

• https://orcid.org/0000-0002-3969-2273

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).