A Neural-Network-Based Autonomous Underwater Vehicle Guidance System

To solve the nonlinear Bayesian estimation problem in underwater terrain-aided navigation, a terrain-aided navigation method based on improved Gaussian sum particle filter is proposed. This method approximates the Bayesian function using multiple Gaussian components, and the components can be obtained by radial basis function neural network. This method has no resampling process, the particle depletion of particle filtering is eliminated in principle. The simulation shows that the proposed method has good matching performance, which is suitable for autonomous underwater vehicle navigation.

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Intelligent Steering Control of an Autonomous Underwater Vehicle

Journal of Navigation ◽

10.1017/s0373463300008973 ◽

2000 ◽

Vol 53 (3) ◽

pp. 511-525 ◽

Cited By ~ 2

Author(s):

R. Sutton ◽

R. S. Burns ◽

P. J. Craven

Keyword(s):

Control Algorithm ◽

Autonomous Underwater Vehicle ◽

Fuzzy Inference ◽

Underwater Vehicle ◽

Guidance System ◽

Line Of Sight ◽

Steering Control ◽

Inference System ◽

Sea Current ◽

Proportional Derivative Controller

This paper considers the development of three autopilots for controlling the yaw responses of an autonomous underwater vehicle model. The autopilot designs are based on the adaptive network-based fuzzy inference system (ANFIS), a simulated, annealing-tuned control algorithm and a traditional proportional-derivative controller. In addition, each autopilot is integrated with a line-of-sight (LOS) guidance system to test its effectiveness in steering round a series of waypoints with and without the presence of sea current disturbance. Simulation results are presented that show the overall superiority of the ANFIS approach.

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Applying the Artificial Neural Network to Estimate the Drag Force for an Autonomous Underwater Vehicle

Open Journal of Fluid Dynamics ◽

10.4236/ojfd.2014.43025 ◽

2014 ◽

Vol 04 (03) ◽

pp. 334-346 ◽

Cited By ~ 1

Author(s):

Ehsan Yari ◽

Ahmadreza Ayoobi ◽

Hassan Ghassemi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Drag Force ◽

Autonomous Underwater Vehicle ◽

Underwater Vehicle ◽

Artificial Neural

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Control with Neural Network For Autonomous Underwater Vehicle

Journal of the Society of Naval Architects of Japan ◽

10.2534/jjasnaoe1968.1989.166_503 ◽

1989 ◽

Vol 1989 (166) ◽

pp. 503-511 ◽

Cited By ~ 7

Author(s):

Teruo Fujii ◽

Tamaki Ura

Keyword(s):

Neural Network ◽

Autonomous Underwater Vehicle ◽

Underwater Vehicle

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Autonomous underwater vehicle precise motion control for target following with model uncertainty

International Journal of Advanced Robotic Systems ◽

10.1177/1729881417719808 ◽

2017 ◽

Vol 14 (4) ◽

pp. 172988141771980 ◽

Cited By ~ 3

Author(s):

Huang Hai ◽

Zhang Guocheng ◽

Qing Hongde ◽

Zhou Zexing

Keyword(s):

Neural Network ◽

Fuzzy Neural Network ◽

Autonomous Underwater Vehicle ◽

Autonomous Underwater Vehicles ◽

External Disturbance ◽

Underwater Vehicle ◽

Parameter Uncertainties ◽

Fuzzy Neural ◽

Recurrent Type

Target following plays an important role in oceanic detection and target capturing for autonomous underwater vehicles. Due to the model nonlinearity and external disturbance, the dynamic model of a portable autonomous underwater vehicle was usually established with parameter uncertainties. In this article, a petri-based recurrent type 2 fuzzy neural network has been built to approximate the unknown autonomous underwater vehicle dynamics. The type 2 fuzzy logic system has been applied to the network to improve the approximation accuracy for systematic nonlinearity, and the petri layer in the network can improve estimation speed and reduce energy consumption. A petri-based recurrent type 2 fuzzy neural network–based adaptive robust controller has been proposed for target tracking. In the offshore experiments, the proposed controller has not only realized stable position and pose control but also successfully followed mobile target on the surface. In the tank underwater experiments, the pipeline target has been successfully followed to further verify the controller performance.

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