Line-of-Sight-Based Guidance and Adaptive Neural Path-Following Control for Sailboats

Deng, Yupu; Zhang, Xianku; Zhang, Guoqing

doi:10.1109/joe.2019.2923502

Cited by 38 publications

(14 citation statements)

References 32 publications

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“…Because the high steering frequency will cause the crew discomfort and reduce the stability of the ship during navigation, the algorithm proposed in this paper has better navigation practicality. For the quantitative purpose, some popular performance indexes (Equation ( 32)) are utilized to evaluate the comparison result [18], e.g., the mean absolute error (MAE) and the mean absolute control effort (MAC). MAE reveals the stabilizing performance of the control system.…”

Section: Comparative Experiments For Control Algorithmmentioning

confidence: 99%

“…It can be observed that the proposed control algorithm has more advantages in many aspects. For the quantitative purpose, some popular performance indexes (Equation ( 32)) are utilized to evaluate the comparison result [18], e.g., the mean absolute error (MAE) and the mean absolute control effort (MAC). MAE reveals the stabilizing performance of the control system.…”

Section: Comparative Experiments For Control Algorithmmentioning

confidence: 99%

“…2021, 9, 1055 2 of 14 between adjacent paths, so as to reduce the overshoot of ship path following. Based on the parameterized LOS framework, a new sailing guidance principle was proposed [18]. In [19], an integral line-of-sight guidance law was presented, which was combined with a sinusoidal gait pattern and a directional controller that steers the robot toward and along the desired path.…”

Section: Introductionmentioning

confidence: 99%

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Radial Basis Function Neural Network Sliding Mode Control for Ship Path Following Based on Position Prediction

Zhang

2021

JMSE

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In the process of ship navigation, due to the characteristics of large inertia and large time delay, overshoot can easily occur in the process of path following. Once the ship deviates from the waypoint, it is prone to grounding and collision. Considering this problem, a sliding mode control algorithm based on position prediction using the radial basis function (RBF) neural network is proposed. The desired heading angle is designed according to a backstepping algorithm. The hyperbolic tangent function is used to design the sliding surface, and the course is controlled by sliding mode control. The second-order Taylor expansion is used to predict the future position, the current error and future error functions are constructed, and the total errors are fed back to the desired heading angle. In the sliding mode control system, the RBF neural network is used to approximate the total unknown term, and a velocity observer is introduced to obtain the surge velocity and sway velocity. To verify the effectiveness of the algorithm, the mathematical model group (MMG) model is used for simulation. The simulation results show the effectiveness and superiority of the designed controller. Therefore, the RBF neural network sliding mode controller based on predicted position has robustness for ship path following.

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Section: Comparative Experiments For Control Algorithmmentioning

confidence: 99%

Section: Comparative Experiments For Control Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Radial Basis Function Neural Network Sliding Mode Control for Ship Path Following Based on Position Prediction

Zhang

2021

JMSE

Self Cite

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“…With the complete mathematical description, the parametric LOS guidance of unmanned sailboats was first proposed in ref. [18], where a sign function described the switching of reference heading angles between tacking and jibing. Concerned with path following of a curved reference path, ref.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the adaptive controllers in refs. [18, 26, 28, 29], the computation burden is largely reduced. Compared with the PID control in refs.…”

Section: Introductionmentioning

confidence: 99%

Path-following and collision-avoidance guidance of unmanned sailboats based on beetle antennae search optimization

Deng

Zhang

et al. 2023

Robotica

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There are few studies on the intelligent guidance of unmanned sailboats, which should coordinate pluralistic tasks at sea in the nature of its maneuvring intractability. To ensure the algorithmic practicability, this paper proposes a path-following and collision-avoidance guidance approach of unmanned sailboats with total formulaic description. The risk-detecting mechanism is fabricated by setting a circular detecting zone and using the time to the closest point of approach. Then, the risk of collision, the path deviation, the speed loss, and the course loss can be judged by constructing the cost functions and applying the distance to closest point of approach. The optimized heading angle is deemed as the one minimizing the aggregate cost functions, which is sought by applying and improving the beetle antennae search (BAS) algorithm. In the proposed modified BAS, the searching step is redesigned to enhance the searching efficiency. To ensure the convergence of the real heading angle to the reference, the backstepping-based control law is fabricated for the high-order sailboat model and in the linear form. The control parameters are offline optimized through the modified BAS. Compared with the adaptive control, this controller can guarantee more computation simplicity and the optimized control performance. Finally, simulation corroborates that the sailboat can successfully complete path following and collision avoidance while encountering multiple static and moving obstacles under the proposed schemes.

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Robust Collision Avoidance and Path‐Following of USVs With Reduced Conservativeness: A Control Barrier Function‐Based Approach

Wen,

Fu,

et al. 2024

Journal of Field Robotics

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The collision avoidance and path‐following problem is fundamental for unmanned surface vehicles (USVs) to accomplish various tasks in different water environments. However, addressing this issue is challenging, as USVs are inevitably affected by environmental disturbances in practice. In this study, we address the robust collision avoidance path‐following problem for USVs with unknown bounded environmental disturbances by using the control barrier function (CBF)‐based approach. To reduce the conservativeness for the collision avoidance actions, the elliptical shape of the USV is considered when designing the control law. Furthermore, a high‐order control barrier function (HOCBF)‐based approach is proposed to achieve the robust collision avoidance with respect to both static and dynamic obstacles. Specifically, a nominal robust path‐following controller is first designed utilizing the predefined‐time observers without considering the collision avoidance requirement. Then, by considering the elliptical USV and the circular obstacles, collision avoidance input constraints are derived by the proper HOCBFs. Finally, a local quadratic programming (QP)‐based controller is proposed to achieve robust collision avoidance and path‐following of the USV. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed control strategy.

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Line-of-Sight-Based Guidance and Adaptive Neural Path-Following Control for Sailboats

Cited by 38 publications

References 32 publications

Radial Basis Function Neural Network Sliding Mode Control for Ship Path Following Based on Position Prediction

Radial Basis Function Neural Network Sliding Mode Control for Ship Path Following Based on Position Prediction

Path-following and collision-avoidance guidance of unmanned sailboats based on beetle antennae search optimization

Robust Collision Avoidance and Path‐Following of USVs With Reduced Conservativeness: A Control Barrier Function‐Based Approach

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