JOURNAL ARTICLE
Ocean Currents Compensation‐Based IAILOS‐ROESOs Guidance and Adaptive Sliding Mode Path Following Control for Unmanned Surface Vehicles.
Published In: International Journal of Adaptive Control & Signal Processing, 2025, v. 39, n. 4. P. 692 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Zhang, Huang; He, Zhiping; Wang, Guofeng; Fan, Yunsheng; Song, Baojian 3 of 3
Abstract
This article studies the problem of path following for unmanned surface vehicles (USVs) with ocean currents and input saturation and proposes an ocean currents compensation‐based improved adaptive integral line‐of‐sight with reduced‐order expanded state observers (IAILOS‐ROESOs) guidance and adaptive integral sliding mode control (AISMC) compound guidance‐control method. In the guidance module, an IAILOS‐ROESOs guidance law is presented to estimate ocean currents of varying strengths, compensating for the effects of ocean currents at the kinematics level. Then, the AISMC law with the RBF neural network and parameter update law are introduced to approximate the lumped disturbances and estimate their bounds of the estimation error in the control module. Meanwhile, integrating nonlinear differential estimators and improved auxiliary dynamic systems with a smoothly switching function, achieves the differential signal filtering and input saturation, and the hyperbolic tangent saturation function with adjustable parameters is further used to improve the robustness of the system. Theoretical analysis indicates that all errors converge to zero. Finally, the effectiveness of the control strategy is verified through comparative simulations. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Adaptive Control & Signal Processing. 2025/04, Vol. 39, Issue 4, p692
- Document Type:Article
- Subject Area:Oceanography
- Publication Date:2025
- ISSN:0890-6327
- DOI:10.1002/acs.3966
- Accession Number:184403809
- Copyright Statement:Copyright of International Journal of Adaptive Control & Signal Processing is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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