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Abstract

As the demand for underwater robots in complex environments continues to grow, research on their agile motion capabilities becomes increasingly crucial. This paper focuses on the design and agile motion control of autonomous underwater vehicles (AUVs) operating in subsea environments, addressing key issues such as structural design, system modeling, and control algorithm development. An optimization model for the arrangement of propellers is formulated and solved using a Sequential Quadratic Programming (SQP) algorithm. Computational Fluid Dynamics (CFD) software is employed for hydrodynamic analysis and shape optimization. A novel adaptive event-triggered nonlinear model predictive control (AET-NMPC) algorithm is proposed and compared with traditional Cascaded Proportional–Integral–Derivative (PID) control and event-triggered cascaded PID control algorithms. Simulation and experimental results demonstrate that the AET-NMPC algorithm significantly enhances the response capability and control accuracy of underwater robots in complex tasks, with the trajectory tracking error being reduced to 4.89%. This study provides valuable insights into the design and control strategies for AUVs, paving the way for more sophisticated underwater operations in challenging environments.

Details

1009240
Title
Adaptive Event-Triggered Predictive Control for Agile Motion of Underwater Vehicles
Author
Wang, Bo 1 ; Peng Junchao 2 ; Zhou, Jing 3   VIAFID ORCID Logo  ; Zhao, Liming 1 

 College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; [email protected] (B.W.); [email protected] (L.Z.) 
 Ocean College, Zhejiang University, Zhoushan 316021, China; [email protected], Hainan Institute, Zhejiang University, Sanya 572025, China 
 College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; [email protected] (B.W.); [email protected] (L.Z.), Hainan Institute, Zhejiang University, Sanya 572025, China 
Volume
13
Issue
6
First page
1072
Number of pages
20
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
Publication subject
e-ISSN
20771312
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-05-28
Milestone dates
2025-04-17 (Received); 2025-05-27 (Accepted)
Publication history
 
 
   First posting date
28 May 2025
ProQuest document ID
3223914672
Document URL
https://www.proquest.com/scholarly-journals/adaptive-event-triggered-predictive-control-agile/docview/3223914672/se-2?accountid=208611
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-07-24
Database
ProQuest One Academic