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Adaptive Event-Triggered Predictive Control for Agile Motion of Underwater Vehicles

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Published in:Journal of Marine Science and Engineering vol. 13, no. 6 (2025), p. 1072-1091
Main Author: Wang, Bo
Other Authors: Peng Junchao, Zhou, Jing, Zhao, Liming
Published:
MDPI AG
Subjects:
Software
Structural engineering
Accuracy
Proportional integral derivative
Hydrodynamics
Fluid dynamics
Ocean currents
Algorithms
Propellers
Optimization
Task complexity
Quadratic programming
Ocean bottom
Predictive control
Underwater robots
Movement
Motion control
Structural design
Design
Tracking errors
Efficiency
Optimization models
Autonomous underwater vehicles
Control theory
Control algorithms
Shape optimization
Robot dynamics
Robots
Underwater exploration
Underwater vehicles
Computational fluid dynamics
Nonlinear control
Robot control
Environmental
Online Access:Citation/Abstract
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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.
ISSN:2077-1312
DOI:10.3390/jmse13061072
Source:Engineering Database