Abstract
The control of the ship's course determines the correctness of navigation, ensures the correct destination, and plays a significant role in the navigation of the ship. Based on RBF neural network and ship mathematical model, a predictive controller using Euclidean distance to calculate center vector for network learning is designed to predict and adjust the three parameters of PID in real time and online, so as to obtain the optimal PID parameters faster. By supervised learning ensuring the course to meet the requirements and the stable navigation of the ship. Then the simulation experiment is carried out to verify the accuracy of the designed controller in Simulink environment of MATLAB software. Compared with the traditional PID controller, this method of this paper reduces the overshoot of the system and the setting time, and improves the accuracy of the ship's course automatic control simultaneously. In addition, the simulation results of ship course control simulation demonstrate the better adaptability, robustness and anti-interference ability of the intelligent PID control strategy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
