Thruster Interactions on Autonomous Underwater Vehicles

2009 ◽  
Author(s):  
Alistair R. Palmer ◽  
Grant E. Hearn ◽  
Peter Stevenson
Keyword(s):  
Hydrodynamic Interaction ◽  
Control Strategies ◽  
Dynamic Performance ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Interaction Effects ◽  
Dynamic Positioning ◽  
Surface Vessels ◽  
Control Devices ◽  
And Control

Autonomous underwater vehicles are a developing technology capable of undertaking a wide variety of different tasks. The development of these vehicles is aided by the use of simulations of their performance. These simulations require accurate modelling of the propulsion and control devices employed to calculate the response of a vehicle to different situations and control strategies. Simulations of underwater vehicles tend to include models of the dynamic performance of the thrusters employed, however, the simulations neglect some of the hydrodynamic interaction effects. These interaction effects include thruster–hull and thruster–thruster interactions similar to those encountered on dynamic positioning surface vessels. This paper assesses these effects for autonomous underwater vehicles and, where appropriate, suggests models for use in simulations.

scholarly journals Autonomous Underwater Vehicles and Field of View in Underwater Operations

2021 ◽  
Vol 9 (3) ◽  
pp. 277
Author(s):  
Isaac Segovia Ramírez ◽  
Pedro José Bernalte Sánchez ◽  
Mayorkinos Papaelias ◽  
Fausto Pedro García Márquez
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Field Of View ◽  
Guidance System ◽  
Control Algorithms ◽  
Deep Waters ◽  
Underwater Cameras ◽  
Optimal Resolution ◽  
Acquisition Processes ◽  
And Control

Submarine inspections and surveys require underwater vehicles to operate in deep waters efficiently, safely and reliably. Autonomous Underwater Vehicles employing advanced navigation and control systems present several advantages. Robust control algorithms and novel improvements in positioning and navigation are needed to optimize underwater operations. This paper proposes a new general formulation of this problem together with a basic approach for the management of deep underwater operations. This approach considers the field of view and the operational requirements as a fundamental input in the development of the trajectory in the autonomous guidance system. The constraints and involved variables are also defined, providing more accurate modelling compared with traditional formulations of the positioning system. Different case studies are presented based on commercial underwater cameras/sonars, analysing the influence of the main variables in the measurement process to obtain optimal resolution results. The application of this approach in autonomous underwater operations ensures suitable data acquisition processes according to the payload installed onboard.

scholarly journals A Distributed Algorithm for Cooperative 3D Exploration under Communication Constraints

2013 ◽  
Vol 4 (4) ◽  
Author(s):  
Ravi Kulan Rathnam ◽  
Andreas Birk
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
High Fidelity ◽  
Optimization Strategy ◽  
Complete Control ◽  
Exploration Process ◽  
Communication Range ◽  
Navigation And Control ◽  
3D Exploration ◽  
And Control

AbstractAn algorithm for distributed exploration in 3D is presented which always keeps the robots within communication range of each other. The method is based on a greedy optimization strategy that uses a heuristic utility function. This makes it computationally very efficient but it can also lead to local minimums; but related deadlocks can be easily detected during the exploration process and there is an efficient strategy to recover from them. The exploration algorithm is integrated into a complete control infrastructure for Autonomous Underwater Vehicles (AUV) containing sensors, mapping, navigation, and control of actuators. The algorithm is tested in a high fidelity simulator which takes into account the dynamics of the robot, and simulates the required sensors. The effect of the communication range and the number of robots on the algorithm is investigated.

scholarly journals Recent Advances in Bidirectional Modeling and Structural Control

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Satyam Paul ◽  
Wen Yu ◽  
Xiaoou Li
Keyword(s):  
Control Systems ◽  
Structural Control ◽  
Seismic Waves ◽  
Control Strategies ◽  
Optimal Placement ◽  
Modeling And Control ◽  
Control Devices ◽  
Identification Techniques ◽  
And Control ◽  
Bidirectional Modeling

This paper provides an overview of building structure modeling and control under bidirectional seismic waves. It focuses on different types of bidirectional control devices, control strategies, and bidirectional sensors used in structural control systems. This paper also highlights the various issues like system identification techniques, the time-delay in the system, estimation of velocity and position from acceleration signals, and optimal placement of the sensors and control devices. The importance of control devices and its applications to minimize bidirectional vibrations has been illustrated. Finally, the applications of structural control systems in real buildings and their performance have been reviewed.

scholarly journals Tracking Control of Autonomous Underwater Vehicles with Acoustic Localization and Extended Kalman Filter

2021 ◽  
Vol 11 (17) ◽  
pp. 8038
Author(s):  
Dongzhou Zhan ◽  
Huarong Zheng ◽  
Wen Xu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Tracking Control ◽  
Autonomous Underwater Vehicles ◽  
Acoustic Signals ◽  
Underwater Vehicles ◽  
Ocean Currents ◽  
Acoustic Localization ◽  
And Control ◽  
Constraints Model

The absence of global positioning system (GPS) signals and the influence of ocean currents are two of the main challenges facing the autonomy of autonomous underwater vehicles (AUVs). This paper proposes an acoustic localization-based tracking control method for AUVs. Particularly, three buoys that emit acoustic signals periodically are deployed over the surface. Times of arrivals of these acoustic signals at the AUV are then obtained and used to calculate an estimated position of the AUV. Moreover, the uncertainties involved in the localization and ocean currents are handled together in the framework of the extended Kalman filter. To deal with system physical constraints, model predictive control relying on online repetitive optimizations is applied in the tracking controller design. Furthermore, due to the different sampling times between localization and control, the dead-reckoning technique is utilized considering detailed AUV dynamics. To avoid using the highly nonlinear and complicated AUV dynamics in the online optimizations, successive linearizations are employed to achieve a trade-off between computational complexity and control performance. Simulation results show that the proposed algorithms are effective and can achieve the AUV tracking control goals.

scholarly journals Modeling of Autonomous Underwater Vehicles with Multi-Propellers Based on Maximum Likelihood Method

2020 ◽  
Vol 8 (6) ◽  
pp. 407
Author(s):  
Feiyan Min ◽  
Guoliang Pan ◽  
Xuefeng Xu
Keyword(s):  
Maximum Likelihood ◽  
Controller Design ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Likelihood Method ◽  
Hydrodynamic Characteristics ◽  
Identification Algorithm ◽  
Modeling And Control ◽  
Good Convergence ◽  
And Control

The hydrodynamic characteristics of multi-propeller autonomous underwater vehicles (AUV) is usually complicated and it is difficult to obtain an accurate mathematical model. A modeling method based on CFD calculation and maximum likelihood identification algorithm is proposed for this problem. Firstly, rough hydrodynamic parameters of AUV hull are obtained by CFD calculation. Secondly, on the basis of rough parameters, a maximum likelihood identification algorithm is proposed to adjust the parameters and improve the model precision. Besides, the method to improve the convergence of identification algorithm is analyzed by considering the characteristics of AUV model structure. Finally, the identification algorithm and identification results were validated with experimental data. It was found that this method has good convergence and adaptability. In particular, the identification results of turning force and torque parameters are highly consistent in different identification experiments, which indicates that this method can well extract the maneuvering characteristics of AUVs, thus contributing to the controller design of AUVs. The research of this paper has potential application for the modeling and control of multi-propeller AUVs.

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