Ship Steering Adaptive CGS Control Based on EKF Identification Method

Guan, Wei; Peng, Haowen; Zhang, Xianku; Sun, Hongyu

doi:10.3390/jmse10020294

Cited by 14 publications

(5 citation statements)

References 24 publications

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“…In addition, mechanical deterioration such as to the rudder pintle or axle may induce uncertainties in the ship motion model together with the influence of external environmental factors [9]. The ship maneuverability Nomoto index for identifying the performance of a ship's steering controller is also taken as the key to the study, and an adaptive robust ship's steering controller based on a closed-loop gain shaping (CGS) scheme and an extended Kalman filter (EKF) online identification method is designed [10]. Therefore, it is essential to find sophisticated solutions to problems like taking uncertainty and the dynamics of model parameters into account to establish the mathematical motion model of large ships with stable performance on the heading control and model robustness.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Method for Ship Heading Control Using Motion Model Prediction and Fractional Order Proportion Integration Differentiation Controller

Shi,

Chen,

Chen

2023

JMSE

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Due to the influence of the natural environment, it is very challenging to control the movement of ships to navigate safely and avoid potential risks induced by external environmental factors, especially for the development of autonomous ships in inland or restricted waterways. In this research, we propose an integrated approach for ship heading control that improves the timeliness and robustness of navigation. Recursive least squares and backward propagation neural networks are utilized to identify the ship motion model parameters under the influence of external factors and predict their development in real time. A particle swarm optimization-integrated Fractional Order Proportion Integration Differentiation (FOPID) controller is then designed based on the dynamically identified motion model to achieve accurate heading control for ships navigating in restricted waterways. A case study was conducted based on the Korea Venture Large Crude Carrier 2 (KVLCC2) model to verify the effectiveness, and a comparison between the conventional FOPID controller and the improved FOPID controller was also conducted. The results indicate that the proposed identification–prediction–optimization FOPID controller has faster speed on stabilization and has higher robustness against external influences, which could provide added value for the development of a motion controller for the autonomous ship for inland and restricted waterway navigation.

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Section: Related Workmentioning

confidence: 99%

An Integrated Method for Ship Heading Control Using Motion Model Prediction and Fractional Order Proportion Integration Differentiation Controller

Shi,

Chen,

Chen

2023

JMSE

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“…Generally, state information is used as the input of the network for the improved SAC algorithm. The states of the network can be expressed as Equation (20):…”

Section: Construction Of Neural Networkmentioning

confidence: 99%

“…Finally, Section 5 concludes the study. Overall, the proposed intelligent SMASV navigation system comprises four components: the path planner, the sensor module, the decision-making module, and the control module [20]. The planning system refers to the path-planning process before sailing.…”

mentioning

confidence: 99%

Autonomous Navigation Decision-Making Method for a Smart Marine Surface Vessel Based on an Improved Soft Actor–Critic Algorithm

Cui

Guan

Zhang

et al. 2023

JMSE

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In this study, an intelligent hybrid algorithm based on deep-reinforcement learning (DRL) is proposed to achieve autonomous navigation and intelligent collision avoidance for a smart autonomous marine surface vessel (SMASV). First, the kinematic model of the SMASV is used, and clauses 13 to 17 of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) are introduced. Then, the electronic chart is rasterized and used for path planning. Next, states, actions, and reward functions are designed, and collision avoidance strategies are formulated. In addition, a temperature factor and a constrained loss function are used to improve the soft actor–critic (SAC) algorithm. This improvement reduces the challenges of hyperparameter adjustment and improves sampling efficiency. By comparing the improved SAC algorithm with other deep-reinforcement learning (DRL) algorithms based on strategy learning, it is proved that the improved SAC algorithm converges faster than the other algorithms. During the experiment, some unknown obstacles are added to the simulation environment to verify the collision-avoidance ability of the trained SMASV. Moreover, eight sea areas are randomly selected to verify the generalization ability of the intelligent-navigation system. The results show that the proposed method can plan a path for the SMASV accurately and effectively, and the SMASV decision-making behavior in the collision-avoidance process conforms to the COLREGs in both unknown and dynamic environments.

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“…In building a complete set of the intelligent smart marine autonomous surface ship (SMASS) decision-making systems, it was necessary to clarify the components of the system, the functions of each part, and the relationship between different parts [ 17 ]. There are three parts included in the intelligent smart marine autonomous surface ship (SMASS) decision-making system, namely, the sensing part, the decision-making part, and the control part, as shown in Figure 1 .…”

Section: Intelligent Ship Decision System and Ship Mathematical Modelmentioning

confidence: 99%

Intelligent Smart Marine Autonomous Surface Ship Decision System Based on Improved PPO Algorithm

Guan

Cui

Zhang

2022

Sensors

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With the development of artificial intelligence technology, the behavior decision-making of an intelligent smart marine autonomous surface ship (SMASS) has become particularly important. This research proposed local path planning and a behavior decision-making approach based on improved Proximal Policy Optimization (PPO), which could drive an unmanned SMASS to the target without requiring any human experiences. In addition, a generalized advantage estimation was added to the loss function of the PPO algorithm, which allowed baselines in PPO algorithms to be self-adjusted. At first, the SMASS was modeled with the Nomoto model in a simulation waterway. Then, distances, obstacles, and prohibited areas were regularized as rewards or punishments, which were used to judge the performance and manipulation decisions of the vessel Subsequently, improved PPO was introduced to learn the action–reward model, and the neural network model after training was used to manipulate the SMASS’s movement. To achieve higher reward values, the SMASS could find an appropriate path or navigation strategy by itself. After a sufficient number of rounds of training, a convincing path and manipulation strategies would likely be produced. Compared with the proposed approach of the existing methods, this approach is more effective in self-learning and continuous optimization and thus closer to human manipulation.

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Ship Steering Adaptive CGS Control Based on EKF Identification Method

Cited by 14 publications

References 24 publications

An Integrated Method for Ship Heading Control Using Motion Model Prediction and Fractional Order Proportion Integration Differentiation Controller

An Integrated Method for Ship Heading Control Using Motion Model Prediction and Fractional Order Proportion Integration Differentiation Controller

Autonomous Navigation Decision-Making Method for a Smart Marine Surface Vessel Based on an Improved Soft Actor–Critic Algorithm

Intelligent Smart Marine Autonomous Surface Ship Decision System Based on Improved PPO Algorithm

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