Ship Motion Control Based on AMBPS-PID Algorithm

Wang, Le; Wu, Qing; Liu, Jialun; Li, Shijie; Negenborn, Rudy R.

doi:10.1109/access.2019.2960098

Cited by 9 publications

(9 citation statements)

References 16 publications

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“…The change strategy of inertia weight enhances the global search and local search capabilities of the method and applies the improved BAS algorithm to the Economic load distribution problem. Based on the BAS algorithm, Wang [59] designed the adaptive mutated beetle particle swarm algorithm, replacing artificial experience with a new optimization algorithm. The optimal control parameters can be quickly determined in the control algorithm, and the heading angle control under the optimal parameters can be realized.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

An Improved Beetle Swarm Optimization Algorithm for the Intelligent Navigation Control of Autonomous Sailing Robots

et al. 2021

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Section: Evolutionary Algorithmsmentioning

confidence: 99%

An Improved Beetle Swarm Optimization Algorithm for the Intelligent Navigation Control of Autonomous Sailing Robots

et al. 2021

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“…External uncontrollable disturbances in the movement of cargo destabilize the loading process as well as create precedents for the negative influence of the human factor. Some works [11][12][13][14][15] present the results on improving mathematical modeling when controlling the vessel movement by automated systems.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Actually, different tasks of dynamic positioning are distinguished when controlling the pitch, lateral, rolling motions of the ship's hull. These are Dynamic Positioning (DP), Dynamic Position's Alteration (DA), Dynamic Tracking (DT) [11,13].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…In this case, the solution of the regulation problem, as the task of stabilization of the loading process parameters, requires identifying an adequate control law by known methods. This is the approach applied in some researches [11,12,19], but the problem of loading Heavy Lift vessels involves a significant increase in safety requirements for operations, and necessitates the synthesis of movement control laws in the "vessel-crane-cargo" system. So, the methodology based on the synthesis of control laws involves regulation compliance at risk and time constraints.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Development of control model for loading operations on heavy lift vessels based on inverse algorithm

Solovey¹,

Ben²,

Dudchenko³

et al. 2020

EEJET

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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 tons

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“…Ship docking is known as one of the most challenging tasks [1]- [4], inappropriate ship maneuvers may lead to collision accidents with the quayside, which causes losses of lives and property. To ensure maritime safety, as well as to contribute to the development of smart ships and autonomous shipping, automatic ship docking techniques are required.…”

Section: Introductionmentioning

confidence: 99%

Automatic Docking for Underactuated Ships Based on Multi-Objective Nonlinear Model Predictive Control

Liu

Negenborn

et al. 2020

IEEE Access

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Autonomous shipping refers to the ability of a ship to independently control its own actions while transporting cargo from one port to another, which places higher requirements on ship motion control methods. When a ship enters a port, it is important to ensure that the ship sails from the fairway area to the assigned position at the berth with a desirable speed and that it finally stops at the desired position. Ship docking is known as one of the most challenging tasks due to the non-linearity of low-speed ship movements and the high requirements on collision avoidance with the quayside. This paper proposes a nonlinear model predictive control (NMPC)-based approach for underactuated ships, providing optimal ship rudder angles and propeller revolution rate to automate the ship docking process. At each sampling instant, a finite horizon optimal control problem is formulated based on a nonlinear ship maneuverability model. A lexicographic multi-objective optimization strategy is proposed in the design of the NMPC controller, saving the efforts on control parameters tuning. Simulation experiments are carried out to evaluate the effectiveness of the proposed approach. INDEX TERMS Automatic docking, motion control, nonlinear model predictive control.

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Ship Motion Control Based on AMBPS-PID Algorithm

Cited by 9 publications

References 16 publications

An Improved Beetle Swarm Optimization Algorithm for the Intelligent Navigation Control of Autonomous Sailing Robots

An Improved Beetle Swarm Optimization Algorithm for the Intelligent Navigation Control of Autonomous Sailing Robots

Development of control model for loading operations on heavy lift vessels based on inverse algorithm

Automatic Docking for Underactuated Ships Based on Multi-Objective Nonlinear Model Predictive Control

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