Course keeping control strategy for large oil tankers based on nonlinear feedback of swish function

Gao, Shihang; Zhang, Xianku

doi:10.1016/j.oceaneng.2021.110385

Cited by 15 publications

(3 citation statements)

References 5 publications

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“…It can be derived from the linearized whole-ship model, and its parameters (the maneuverability indices) consist of some linear hydrodynamic derivatives in the whole-ship model. With the response model, the three degrees of freedom (3-DoF) model of ship maneuvering motion on the horizontal plane is simplified to the mathematical relationship between the yaw rate and the rudder angle which has the advantages of a simple structure and the ability to capture the response characteristics of ship steering motion to the control input and has been extensively used to predict ship steering motion as well as to design ship autopilots [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Modeling and Control of Ship Steering Motion Based on Local Gaussian Process Regression

Ouyang,

Zou,

Zou

2023

JMSE

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This paper aims to study the nonparametric modeling and control of ship steering motion. Firstly, the black box response model is derived based on the Nomoto model. Then, the establishment of a nonparametric response model and prediction of ship steering motion are realized by applying the local Gaussian process regression (LGPR) algorithm. To assess the performance of LGPR, two cases are studied, including a Mariner class vessel by using simulation data and a KVLCC2 tanker model by using experimental data. The results reveal that the response model identified by LGPR presents good prediction accuracy and low computational burden. Finally, the identified response model is used as the basis for developing the ship heading controller, and the results demonstrate that the proposed controller is able to achieve good dynamic performance.

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Section: Introductionmentioning

confidence: 99%

Nonparametric Modeling and Control of Ship Steering Motion Based on Local Gaussian Process Regression

Ouyang,

Zou,

Zou

2023

JMSE

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“…Course control for USVs is an essential and foundational issue. There are four main types of mathematical models for USV course control, such as Nomoto [1], Norribin [2,3], MMG [4] and Fossen [5]. Based on these mathematical models, many methods have been applied to solve the course control, achieving desirable theoretical results, mainly including PID control [6], ADRC [7], dynamic surface control [8,9], sliding mode control [10,11], intelligent control [12,13] and other methods.…”

Section: Introductionmentioning

confidence: 99%

Model-Free Adaptive Sliding Mode Control Method for Unmanned Surface Vehicle Course Control

Liu,

Ye,

Yang

2023

JMSE

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A novel data-driven-based adaptive sliding-mode control scheme is proposed for unmanned surface vehicle course control in the presence of disturbances. The proposed method utilizes the model-free adaptive control (MFAC) theory. On account of the unknown dynamics of the USV course system, the control scheme is only established by online input and output information of the system. Based on a model-free adaptive control scheme, the system disturbance estimation technique is applied to compensate for the disturbances in the established compact form dynamic linearization data model. The controller is designed and combined with the sliding mode method, and a second-order switching surface with a fast terminal sliding function is employed to achieve finite-time convergence. Furthermore, an analysis of the stability of the control system is provided. Finally, MATLAB simulations are implemented to verify the validity and robustness of the proposed control scheme by comparing it with PID and typical model-free adaptive sliding mode control.

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“…In recent years, many modern control theory methods have been widely used in the design of the navigation control system of the USV [3][4][5], including adaptive output-feedback control, adaptive fuzzy control [6], adaptive neural decentralized control [7], feedback fuzzy tracking control for discrete-time [8], and so on [9,10]. Literature [11] studies static output-feedback tracking control problem for discrete-time nonlinear networked systems.…”

Section: Introductionmentioning

confidence: 99%

Research on Heading Control of USV with the Lateral Thruster

Zhang

Yan

Wang

et al. 2022

Mathematical Problems in Engineering

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In order to solve the problem of low speed and stable course tracking of the small unmanned surface vessel (USV), the lateral thruster are added to the driving structure of the USV, and a backstepping control method is designed. By introducing the Lyapunov function, the course tracking error can converge to the region near the desired course and meet the transient and steady performance of the system at the same time. The adaptive control is applied to the lateral thrust control and the closed-loop system remains stable for a finite time. The Lyapunov direct method is used to prove the controller, which greatly simplifies the tedious parameter adjustment problem in traditional control. The simulation results show that the designed controller can make the USV track the specified value stably at low speed and meet the requirements of the USV heading robustness under dynamic conditions.

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Course keeping control strategy for large oil tankers based on nonlinear feedback of swish function

Cited by 15 publications

References 5 publications

Nonparametric Modeling and Control of Ship Steering Motion Based on Local Gaussian Process Regression

Nonparametric Modeling and Control of Ship Steering Motion Based on Local Gaussian Process Regression

Model-Free Adaptive Sliding Mode Control Method for Unmanned Surface Vehicle Course Control

Research on Heading Control of USV with the Lateral Thruster

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