Volume 39, Number 1, February 2021
|Page(s)||216 - 223|
|Published online||09 April 2021|
Path following of ship based on sliding mode control with improved RBF neural network and virtual circle
To address the unmeasured velocity, external disturbance and internal model uncertainty for following the path of an under-actuated ship, the paper presents a sliding mode control method based on the radial basis function(RBF) neural network and the velocity observer. To enhance the RBF performance of approximating the unknown, an arc tangent function was exploited in the RBF neural network to update its weight values. Then, the nonlinear observer was built via the hyperbolic tangent function to deal with the unmeasured velocity of the ship. Furthermore, in order to avoid overshoots when the ship is moving to its way points, the virtual paths of a variable circle based on the turning angle were designed at the joints of the path of the ship to enhance its path following capability. Finally, the simulation results show that the sliding mode controller designed in the paper can force the ship to follow accurately the reference path in case of time-varying disturbances without measured velocity and enhance the path following performance of the ship and the accuracy of the RBF neural network, thus demonstrating its effectiveness.
Key words: path following / sliding mode control / radial basis function neural network / nonlinear observer
关键字 : 船舶路径跟踪 / 滑模控制 / 径向基函数神经网络 / 非线性观测器
© 2021 Journal of Northwestern Polytechnical University. All rights reserved.
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