NDLI: Hydrodynamic and Control Analysis of a Self-Controllable Underwater Towed Vehicle

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Wu, Jiaming Xiong, Xiaohui
Copyright Year	2006
Abstract	The hydrodynamic and control performances of a self-stable controllable underwater towed vehicle developed by South China University of Technology under different depth trajectory control operations are analyzed by means of a proposed hydrodynamic numerical model. The model is established based on LMBP algorithm of neural network theory. Training samples for the neural network model are provided from the experimental data of the vehicle prototype towing experiments conducted in a large-scale ship model towing tank under the manipulation of a depressing wing installed in the vehicle. After the LMBP model is established, a depth trajectory control system for the towed vehicle is designed in order to accomplish vehicle trajectory control. This system is mainly composed of tow parts: a neural network identifier based on genetic algorithm and a fuzzy neural network controller based on genetic algorithm simulated annealing. Hydrodynamic performances of the vehicle under various control operations can then be numerically simulated with the proposed LMBP model and the depth trajectory control system of the towed vehicle. In numerical simulation of trajectory control to the towed vehicle, deflection of the vehicle’s depressing wing is adjusted at every time step by the proposed control system to match the trajectory of the vehicle with a pre-designated one. The value of the deflection is taken as input parameter for the LMBP neural network model, trajectory and attitude behavior of the towed vehicle under the control manipulations can then be predicted by the LMBP model.
Sponsorship	Ocean, Offshore, and Arctic Engineering Division
Starting Page	139
Ending Page	146
Page Count	8
File Format	PDF
ISBN	0791847470
DOI	10.1115/OMAE2006-92108
e-ISBN	0791837777
Volume Number	Volume 2: Ocean Engineering and Polar and Arctic Sciences and Technology
Conference Proceedings	25th International Conference on Offshore Mechanics and Arctic Engineering
Language	English
Publisher Date	2006-06-04
Publisher Place	Hamburg, Germany
Access Restriction	Subscribed
Subject Keyword	Model basin Computer simulation Deflection Artificial neural networks Control equipment Control systems Fuzzy neural nets Vehicles Genetic algorithms Neural network models Wings Algorithms Ships China Simulated annealing Trajectories (physics) Engineering prototypes
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Trajectory and Attitude Control of an Underwater Towed Vehicle

Experimental Observation on a Controllable Underwater Towed Vehicle With Vertical Airfoil Main Body

S plane control based on parameters optimization with simulated annealing for underwater vehicle

T-S norm fuzzy neural network controller for underwater vehicles based on hybrid learning algorithm

GA Optimized Formation Control of Autonomous Underwater Vehicles

Motion control of towed underwater vehicles with movable wings using a high-gain observer based approach

Self-adaptive neuro-fuzzy systems with fast parameter learning for autonomous underwater vehicle control

Self-adaptive recurrent neuro-fuzzy control for an autonomous underwater vehicle

Simulation and Genetic Algorithms for Control Planning of Underwater Vehicle Trajectories

Hydrodynamic and Control Analysis of a Self-Controllable Underwater Towed Vehicle

Similar Documents

Trajectory and Attitude Control of an Underwater Towed Vehicle

Experimental Observation on a Controllable Underwater Towed Vehicle With Vertical Airfoil Main Body

S plane control based on parameters optimization with simulated annealing for underwater vehicle

T-S norm fuzzy neural network controller for underwater vehicles based on hybrid learning algorithm

GA Optimized Formation Control of Autonomous Underwater Vehicles

Motion control of towed underwater vehicles with movable wings using a high-gain observer based approach

Self-adaptive neuro-fuzzy systems with fast parameter learning for autonomous underwater vehicle control

Self-adaptive recurrent neuro-fuzzy control for an autonomous underwater vehicle

Simulation and Genetic Algorithms for Control Planning of Underwater Vehicle Trajectories

Hydrodynamic and Control Analysis of a Self-Controllable Underwater Towed Vehicle