Abstract
In order to further reduce the rudder angle and the steering frequency on the premise of achieving good course-keeping control effect for ships, a concise robust control algorithm is proposed in this paper. The second order closed-loop gain shaping algorithm is employed to design the linear controller first. Then the final control law is achieved by using the nonlinear decoration technique. The stability of the closed-loop system is proved by the Nyquist criterion. Taking training vessel Yukun as a test plant, the simulation experiments under normal sea state and heavy sea state are carried out respectively to verify the effectiveness of the proposed scheme. The results indicate that compared with the existing methods, the proposed control algorithm not only has obvious advantages in energy-saving effect and smoothness, but also has stronger anti-interference ability under heavy sea state. The second-order closed-loop gain shaping controller decorated by bipolar sigmoid function has better robustness and a concise form. Meanwhile its remarkable energy-saving effect and smoothness make steering condition meet the requirements of navigation practice, which is of great significance for ships to realize safe and efficient navigation.
Highlights
Course-keeping control is an important problem of ship motion control field
The first-order CGSA controller decorated by sine function in Ref. [15], which has good energy-saving effect and strong robustness, is selected as reference to compare with the proposed controller
The comparative analysis is based on their course-keeping effects and energy-saving effects
Summary
Course-keeping control is an important problem of ship motion control field. It is the core of automatic navigation for ships in marine transportation. [1], RBF neural network was used to approximate the unknown items of the ship model and an auxiliary system was designed to compensate the input saturation of the steering system. [2], Nussbaum gain function and DSC (Dynamic Surface Control) were employed to design course-keeping controller with unknown control direction. [3] presented a course-keeping controller based on DSC and MLP (Minimal Learning Parameter) technique, which solved the explosion of complexity problem of backstepping method and reduced the online learning burden of neural networks.
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