A Gradient-Based Path Following Control Method for Underactuated Autonomous Marine Vehicle
Due to their underactuation, motion control for autonomous marine vehicles (AMVs) has always been a tricky problem. Most current path following control methods for underactuated autonomous marine vehicles apply geometric projection to map position error to yaw angle in order to decouple the yaw thrust moment in the controller design. However, there will be deviations in the geometric projection because of sideslip, especially when external disturbances exist. Moreover, the performance of the geometric projection method is greatly affected by the look-ahead distance. In this article, a novel path following control method for underactuated AMVs is proposed. This method directly takes the position of a point as the control object, with the process of geometric projection eliminated, thereby getting rid of the negative impact of sideslip. The desired path is modeled as a scalar field, and the commanded position is taken to lie along the direction of the gradient of this scalar field at the kinematics level. At the kinetics level, disturbance rejection capability is constructed by applying disturbance observers and compensating for the estimated disturbances in the control effort. It is proved that all signals in the closed-loop system are uniformly ultimately bounded. Simulation and experimental results demonstrate that the proposed method is effective.
Authors
- Shiming He (ORCID: https://orcid.org/0000-0003-2572-8041)
- Weijun Zhou
- Zishi Xu (ORCID: https://orcid.org/0000-0001-8408-179X)
- Yanjun Li (ORCID: https://orcid.org/0000-0003-4676-6223)
- Xiang Ji (ORCID: https://orcid.org/0000-0002-7234-6460)
Institutions
- Huzhou Normal University (CN)
- Hangzhou City University
- Zhejiang University (CN)
Publication Details
- Journal
- IEEE Journal of Oceanic Engineering
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1109/joe.2025.3529126
- Citations
- 2
- Primary Topic
- Robotic Path Planning Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 2.55
Funders
- National Natural Science Foundation of China