ANFIS-based course-keeping control for ships using nonlinear feedback technique

Zhang, Zhiheng; Zhang, Xianku; Zhang, Guoqing

doi:10.1007/s00773-018-0581-z

Cited by 11 publications

(4 citation statements)

References 15 publications

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“…Data-driven controllers become feasible for ship course-keeping control with the development of data acquisition and processing technologies. Methods adopted are known as expert knowledge controllers [30], artificial neural networks [31][32][33][34][35], neuro-fuzzy systems [36], and multi-agent systems [37]. These controllers are established based on empirical knowledge or navigation data.…”

Section: Introductionmentioning

confidence: 99%

PSO-Based Predictive PID-Backstepping Controller Design for the Course-Keeping of Ships

Lin,

Zheng,

Han

et al. 2024

JMSE

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Ship course-keeping control is of great significance to both navigation efficiency and safety. Nevertheless, the complex navigational conditions, unknown time-varying environmental disturbances, and complex dynamic characteristics of ships pose great difficulties for ship course-keeping. Thus, a PSO-based predictive PID-backstepping (P-PB) controller is proposed in this paper to realize the efficient and rapid course-keeping of ships. The proposed controller takes the ship’s target course, current course, yawing speed, as well as predictive motion parameters into consideration. In the design of the proposed controller, the PID controller is improved by introducing predictive control. Then, the improved controller is combined with a backstepping controller to balance the efficiency and stability of the control. Subsequently, the parameters in the proposed course-keeping controller are optimized by utilizing Particle Swarm Optimization (PSO), which can adaptively adjust the value of parameters in various scenarios, and thus further increase its efficiency. Finally, the improved controller is validated by carrying out simulation tests in various scenarios. The results show that it improves the course-keeping error and time-response specification by 4.19% and 9.71% on average, respectively, which can efficiently achieve the course-keeping of ships under various scenarios.

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Section: Introductionmentioning

confidence: 99%

PSO-Based Predictive PID-Backstepping Controller Design for the Course-Keeping of Ships

Lin,

Zheng,

Han

et al. 2024

JMSE

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“…Theorem. Consider the system (1), and the control law (24) with the estimated parameters update law (19). If Assumptions 1, 2, 3, and 4 are satisfied, then the proposed steering controller can suppress the external disturbance and make the whole ship steering control system semi-globally uniform and ultimately bounded, and the course tracking error can converge to a compact set Ω = e : e ≤ √ ε 0 /a 0 gradually.…”

Section: Stability Analysismentioning

confidence: 99%

“…Additionaly, a series of intelligent ship steering controllers, such as the neural network controller [16], quantum neural network controller [2], expert system controller [17], adaptive fuzzy controller [18], adaptive neuro-fuzzy inference controller [19], and the stateof-the-art artificial intelligence (AI) controller based on deep reinforcement learning [20] were proposed to enhance ship steering controllers' adaptability and self-learning ability. However, due to their strong subjectivity and poor theoretical interpretation, the practical applications of the above intelligent ship steering controllers have not been reported.…”

Section: Introductionmentioning

confidence: 99%

Ship Steering Adaptive CGS Control Based on EKF Identification Method

Guan

Peng

Zhang

et al. 2022

JMSE

Self Cite

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In recent years, marine autonomous surface vessels (MASS) have grown into a ship research issue to increase the level of autonomy of ship behavior decision-making and control while sailing at sea. This paper focuses on the MASS motion control module design that aims to improve the accuracy and reliability of ship steering control systems. Nevertheless, the stochastic sea and wind environment have led to the extensive use of filters and state observers for estimating the ship-motion-related parameters, which are important for ship steering control systems. In particular, the ship maneuverability Nomoto index, which primarily determines the designed ship steering controller’s performance, cannot be observed directly due to the model errors and the external environment disturbance in the process of sailing. Hence, an adaptive robust ship steering controller based on a closed-loop gain shaping (CGS) scheme and an extended Kalman filter (EKF) on-line identification method is explored in this paper. To verify the effectiveness of the proposed steering controller design scheme, the motor vessel YUKUN was taken as the control plant and a series of simulation experiments were carried out. The results show the advantages of the dynamic response performance of the proposed steering controller compared with the classical PD and traditional CGS controllers. Therefore, the proposed adaptive CGS steering controller would be a good solution for MASS motion control module design.

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“…The authors of [ 16 ] present numerical simulations that show Jang’s scheme is an effective and very simple method for identification of the characteristics of ship roll motion and produce relatively reasonable solutions. In the 2019 paper [ 17 ] an analysis of two methods was performed, a nonlinear Nomoto model and a maneuvering model group (MMG) model of Yupeng ship were established and verified by the turning trial at sea, then an adaptive neuro-fuzzy inference system (ANFIS) controller was trained by learning the actual ship trial data.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Multidimensional Controllers Designated to Steering of the Motions of Ship at Low Speed

Gierusz

Rybczak

2020

Sensors

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The article described two full multidimensional controllers applied to steer a real vessel named ‘Blue Lady’ that is used by the Foundation for Safety of Navigation and Environment Protection at its training and research facility loacted at Silm lake in Poland. Both controllers were based on different approaches, but finally gave similar results. The first part describes the object to be controlled which is a training ship used for training of navigators in various conditions, areas and manoeuvres. This is followed by a short description of the theory for both controllers, Robust and Linear Matrix Inequalities (LMI). Next real time trials are described, which are 3 different manouvers for low velocities, executed by both LMI and Robust contrllers. In these trials ‘Blue Lady’ velocities, silhouete trajectory ans wind data are recorded. Finally the quality of work for both controllers is collected in two tables.

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ANFIS-based course-keeping control for ships using nonlinear feedback technique

Cited by 11 publications

References 15 publications

PSO-Based Predictive PID-Backstepping Controller Design for the Course-Keeping of Ships

PSO-Based Predictive PID-Backstepping Controller Design for the Course-Keeping of Ships

Ship Steering Adaptive CGS Control Based on EKF Identification Method

Effectiveness of Multidimensional Controllers Designated to Steering of the Motions of Ship at Low Speed

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