Review of Course Keeping Control System for Unmanned Surface Vehicle

Author:

Azzeri M. N.,Adnan F. A.,Md. Zain M. Z.

Abstract

This paper presents a review of research work done on various aspects of control system approaches of unmanned surface vehicle (USV) in order to improve the course keeping performance. Various methods have been used to produce a course keeping control system for manoeuvring system of USV. However, the review reveals that the adaptive backstepping control system is a powerful tool for the design of controllers for nonlinear systems or transformable to form a tight feedback parameter. It is very suitable for the automated control system of USV in relative motion that involves the disturbances from waves and wind. Fuzzy logic control also had been suggested as an alternative approach for complex systems with uncertain dynamics and those with nonlinearities. This method does not rely on the mathematical models, but the heuristic approach. Further studies may be conducted to combine the control method approach mentioned above to develop a real time system with robust control laws to the motions of a USV in waves, usually at a specific speed, including station keeping or heading in sinusoidal and irregular waves.  

Publisher

Penerbit UTM Press

Subject

General Engineering

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Marine $\mathcal{X}$: Design and Implementation of Unmanned Surface Vessel for Vision Guided Navigation;2023 21st International Conference on Advanced Robotics (ICAR);2023-12-05

2. Research on Heading Control of Unmanned Surface Vehicle with Variable Parameter Full Format Model-Free Adaptive Algorithm;2023 IEEE 11th International Conference on Computer Science and Network Technology (ICCSNT);2023-10-21

3. Fixed Time Disturbance Observer-Based Trajectory Tracking Control of Unmanned Surface Vessel;2023 42nd Chinese Control Conference (CCC);2023-07-24

4. Deep Reinforcement Learning Based Tracking Control of an Autonomous Surface Vessel in Natural Waters;2023 IEEE International Conference on Robotics and Automation (ICRA);2023-05-29

5. Survey of Deep Learning for Autonomous Surface Vehicles in Marine Environments;IEEE Transactions on Intelligent Transportation Systems;2023-04

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