Controlled course-keeping simulations of a ship under external disturbances

BUDAK, Gökhan; Beji, Serdar

doi:10.1016/j.oceaneng.2020.108126

Cited by 19 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Discussionmentioning

confidence: 99%

“…However, our method did not consider the hydrodynamic interaction between the ships. External hydrodynamic interference, such as waves and currents, is one of the important influencing factors for ship navigation decisions [31,32]. Our method has limitations by determining a reasonable size of the ship domain and avoiding the impact of hydrodynamic interference through navigators.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Risk Identification Method for Ship Navigation in the Complex Waterways via Consideration of Ship Domain

Wang,

Wu,

Chu

et al. 2023

JMSE

View full text Add to dashboard Cite

Collision risk identification is an important basis for intelligent ship navigation decision-making, which evaluates results that play a crucial role in the safe navigation of ships. However, the curvature, narrowness, and restricted water conditions of complex waterways bring uncertainty and ambiguity to the judgment of the danger of intelligent ship navigation situation, making it difficult to calculate such risk accurately and efficiently with a unified standard. This study proposes a new method for identifying ship navigation risks by combining the ship domain with AIS data to increase the prediction accuracy of collision risk identification for ship navigation in complex waterways. In this method, a ship domain model is constructed based on the ship density map drawn using AIS data. Then, the collision time with the target ship is calculated based on the collision hazard detection line and safety distance boundary, forming a method for dividing the danger level of the ship navigation situation. In addition, the effectiveness of this method was verified through simulation of ships navigation in complex waterways, and correct collision avoidance decisions can be made with the Regulations for Preventing Collisions in Inland Rivers of the People’s Republic of China, indicating the advantages of the proposed risk identification method in practical applications.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Risk Identification Method for Ship Navigation in the Complex Waterways via Consideration of Ship Domain

Wang,

Wu,

Chu

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…For instance, a system-based ship manoeuvring simulation of 150 s can be executed in 50 s on a typical Intel Core i7 @3.2 GHz processor [53], whereas a CFD-based simulation of 19 s can take 225 h on an Intel Xeon E5-2680 v2 @2.8 GHz processor [55]. The two most widely used system-based models are the Abkowitz model [56], also known as the whole ship model, which expresses the hydrodynamic forces and moments acting on a ship as Taylor series, and the manoeuvring modelling group (MMG) model [57], also known as the modular model, which decomposes the forces and moments acting on different components, typically the ship hull, propeller, and rudder [58]. Compared to the Abkowitz model that considers the ship as a whole, the MMG model is based on first principles that carry explicit and clear physical meaning of each component including their interactions [59].…”

Section: Ship Manoeuvrabilitymentioning

confidence: 99%

Robust Decision-Making for the Reactive Collision Avoidance of Autonomous Ships against Various Perception Sensor Noise Levels

Lee,

Theotokatos,

Boulougouris

2024

JMSE

View full text Add to dashboard Cite

Autonomous ships are expected to extensively rely on perception sensors for situation awareness and safety during challenging operations, such as reactive collision avoidance. However, sensor noise is inevitable and its impact on end-to-end decision-making has not been addressed yet. This study aims to develop a methodology to enhance the robustness of decision-making for the reactive collision avoidance of autonomous ships against various perception sensor noise levels. A Gaussian-based noisy perception sensor is employed, where its noisy measurements and noise variance are incorporated into the decision-making as observations. A deep reinforcement learning agent is employed, which is trained in different noise variances. Robustness metrics that quantify the robustness of the agent’s decision-making are defined. A case study of a container ship using a LIDAR in a single static obstacle environment is investigated. Simulation results indicate sophisticated decision-making of the trained agent prioritising safety over efficiency when the noise variance is higher by conducting larger evasive manoeuvres. Sensitivity analysis indicates the criticality of the noise variance observation on the agent’s decision-making. Robustness is verified against noise variance up to 132% from its maximum trained value. Robustness is verified only up to 76% when the agent is trained without the noise variance observation with lack of its prior sophisticated decision-making. This study contributes towards the development of autonomous systems that can make safe and robust decisions under uncertainty.

show abstract

“…The problem of course keeping control is highly non-linear in nature and has been studied from a perspective of observed disturbance control using sliding mode control (SMC) approach. The SMC problem for USVs, subjected to, higher order non linear operational disturbances, have been studied with varying control approaches like sliding mode [3][4][5][6]; fuzzy sliding mode [7]; proportional derivative fuzzy [8]; backstepping [9][10][11][12]; backstepping with adaptive radial basis function neural network [13]; sine function-based non-linear feedback [14]; hyperbolic tangent based nonlinear control [15]; sigmoid based nonlinear control [16]; function adaptive neural path following control [17]; model predictive control [18,19]; eventtriggered control approach [20] and non-linear feedback power functions [21].…”

Section: State Of the Artmentioning

confidence: 99%

Adaptive Integral Sliding Mode Based Course Keeping Control of Unmanned Surface Vehicle

González-Prieto¹,

Pérez-Collazo²,

Singh³

2022

JMSE

View full text Add to dashboard Cite

This paper investigates the course keeping control problem for an unmanned surface vehicle (USV) in the presence of unknown disturbances and system uncertainties. The simulation study combines two different types of sliding mode surface based control approaches due to its precise tracking and robustness against disturbances and uncertainty. Firstly, an adaptive linear sliding mode surface algorithm is applied, to keep the yaw error within the desired boundaries and then an adaptive integral non-linear sliding mode surface is explored to keep an account of the sliding mode condition. Additionally, a method to reconfigure the input parameters in order to keep settling time, yaw rate restriction and desired precision within boundary conditions is presented. The main strengths of proposed approach is simplicity, robustness with respect to external disturbances and high adaptability to static and dynamics reference courses without the need of parameter reconfiguration.

show abstract

Controlled course-keeping simulations of a ship under external disturbances

Cited by 19 publications

References 16 publications

Risk Identification Method for Ship Navigation in the Complex Waterways via Consideration of Ship Domain

Risk Identification Method for Ship Navigation in the Complex Waterways via Consideration of Ship Domain

Robust Decision-Making for the Reactive Collision Avoidance of Autonomous Ships against Various Perception Sensor Noise Levels

Adaptive Integral Sliding Mode Based Course Keeping Control of Unmanned Surface Vehicle

Contact Info

Product

Resources

About