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

Guardado en:
Detalles Bibliográficos
Publicado en:Journal of Marine Science and Engineering vol. 13, no. 6 (2025), p. 1072-1091
Autor principal: Wang, Bo
Otros Autores: Peng Junchao, Zhou, Jing, Zhao, Liming
Publicado:
MDPI AG
Materias:
Acceso en línea:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!

MARC

LEADER 00000nab a2200000uu 4500
001 3223914672
003 UK-CbPIL
022 |a 2077-1312 
024 7 |a 10.3390/jmse13061072  |2 doi 
035 |a 3223914672 
045 2 |b d20250101  |b d20251231 
084 |a 231479  |2 nlm 
100 1 |a Wang, Bo  |u College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; 12210111@zju.edu.cn (B.W.); 0921201@zju.edu.cn (L.Z.) 
245 1 |a Adaptive Event-Triggered Predictive Control for Agile Motion of Underwater Vehicles 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a 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. 
653 |a Software 
653 |a Structural engineering 
653 |a Accuracy 
653 |a Proportional integral derivative 
653 |a Hydrodynamics 
653 |a Fluid dynamics 
653 |a Ocean currents 
653 |a Algorithms 
653 |a Propellers 
653 |a Optimization 
653 |a Task complexity 
653 |a Quadratic programming 
653 |a Ocean bottom 
653 |a Predictive control 
653 |a Underwater robots 
653 |a Movement 
653 |a Motion control 
653 |a Structural design 
653 |a Design 
653 |a Tracking errors 
653 |a Efficiency 
653 |a Optimization models 
653 |a Autonomous underwater vehicles 
653 |a Control theory 
653 |a Control algorithms 
653 |a Shape optimization 
653 |a Robot dynamics 
653 |a Robots 
653 |a Underwater exploration 
653 |a Underwater vehicles 
653 |a Computational fluid dynamics 
653 |a Nonlinear control 
653 |a Robot control 
653 |a Environmental 
700 1 |a Peng Junchao  |u Ocean College, Zhejiang University, Zhoushan 316021, China; 22234093@zju.edu.cn 
700 1 |a Zhou, Jing  |u College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; 12210111@zju.edu.cn (B.W.); 0921201@zju.edu.cn (L.Z.) 
700 1 |a Zhao, Liming  |u College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; 12210111@zju.edu.cn (B.W.); 0921201@zju.edu.cn (L.Z.) 
773 0 |t Journal of Marine Science and Engineering  |g vol. 13, no. 6 (2025), p. 1072-1091 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3223914672/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3223914672/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3223914672/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch