Development of control model for loading operations on heavy lift vessels based on inverse algorithm
DOI:
https://doi.org/10.15587/1729-4061.2020.214856Keywords:
optimal control, PID controller, heavy lift cargo, inverse algorithm, loading modelingAbstract
The aim of the work is to develop a method for optimal control of handling operations with heavy lift cargo on sea vessels. Based on the review of scientific research in the field of loading heavy lift cargo, priority directions for improving the automated control systems for cargo handling operations on ships have been determined. Within a scientific hypothesis, it was proposed to synchronize solutions to the problem of ship propulsion control and automated control of heavy lift onboard cranes in order to improve the accuracy of loading processes.
The paper analyzes the dynamic model of the “vessel-crane-cargo” system and the criteria of optimality in the problem of ship regulation-stabilization under minimization of loading time.
An inverse loading algorithm has been developed, based on the principles of the loading control optimization with limiting the choice of motion by linear displacements and turns of the vessel. When executing the inverse algorithm, restrictions associated with the minimization of heeling moments in the “vessel-crane-cargo” system and restrictions associated with the maximum and minimum boom outreach are applied. The study determined the technical feasibility of achieving invariance in the cargo stabilization system with the inverse loading algorithm on heavy lift vessels.
On the basis of the proposed method, simulation modeling of the ship loading process was carried out on simulators at the Kherson State Maritime Academy.
The simulation modeling has shown that the use of the inverse algorithm will reduce the time of cargo operations by 50–70 percent and, as a result, reduce the risk of emergencies when loading the ship. It was also determined that the use of the inverse algorithm is appropriate for cargo of more than 100 tonsReferences
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Copyright (c) 2020 Oleksandr Solovey, Andrii Ben, Sergiy Dudchenko, Pavlo Nosov
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