A neural network approach to ship track-keeping control

Zhang, Yao; Hearn, Grant E.; Sen, Pratyush

doi:10.1109/48.544061

Cited by 62 publications

(20 citation statements)

References 13 publications

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“…These include model reference adaptive control, self-tuning control, optimal control and neural control [1][2][3][4]. In this paper, adaptive fuzzy H 1 control will be used to design a ship autopilot.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy control for the ship steering problem

Rigatos

Tzafestas

2006

Mechatronics

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Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy control for the ship steering problem

Rigatos

Tzafestas

2006

Mechatronics

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“…In particular, this results in improved energy efficiency without sacrificing tracking precision. The robustness of neural network control schemes to hydrodynamic disturbances has been demonstrated in the case of autonomous underwater vehicles as well as large ships, e.g., [15], [18].…”

Section: Dynamics Of Underwater Manipulatorsmentioning

confidence: 99%

A neuro-fuzzy controller for underwater robot manipulators

Pandian

Sakagami

2010

2010 11th International Conference on Control Automation Robotics &Amp; Vision

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Autonomous underwater vehicles are increasingly replacing the prevalent remotely operated vehicle-manipulator systems. Most current generation AUVs are not fitted with manipulators and hence are mainly limited to underwater surveying and surveillance tasks because of the difficulty in the coordinated control of the resulting underwater vehiclemanipulator systems. While several researchers have proposed various techniques for control of AUVs, there is still much research to be done on the precise control of underwater manipulators. This paper presents an intelligent control method for underwater manipulators based on the neuro-fuzzy approach. The controller is composed of fuzzy PD control with feedback gain tuning by linguistic rules. A neural network compensator approximates the dynamics of the multiple degrees of freedom manipulator in decentralized form. The proposed controller has advantages of simplicity of implementation due to decentralized design, precision, and robustness to payload variations and hydrodynamic disturbances. It has lower energy consumption compared to the conventional PD control method. The effectiveness of the proposed controller is illustrated by experimental results for a three degrees of freedom underwater manipulator.

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“…At present, intelligence technologies applying to the subject develop rapidly with great achievements. Neural networks have been used for ship track-keeping in paper [1]. Fuzzy controller and a modified algorithm to control ship track were proposed [2,3].…”

Section: Introductionmentioning

confidence: 99%

Autopilot Design for Unmanned Surface Vehicle Tracking Control

Sun

Ren

et al. 2011

2011 Third International Conference on Measuring Technology and Mechatronics Automation

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With development of intelligence technology, people applied all kinds of intelligent algorithms in ship tracking control. Unmanned surface vehicle (USV) raised less attention due to flexible features more complicated motion system and constantly changing according to the environment. In this work, based on previous achievements, a mathematic model of USV and the corresponding Simulink modules are established and then, Line of sight guidance (LOS) scheme is introduced to implement the feasibility of the ship's tracking control. Finally in Matlab, experimental simulations are conducted, which verify performance and accuracy of the proposed methodology.

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A neural network approach to ship track-keeping control

Cited by 62 publications

References 13 publications

Adaptive fuzzy control for the ship steering problem

Adaptive fuzzy control for the ship steering problem

A neuro-fuzzy controller for underwater robot manipulators

Autopilot Design for Unmanned Surface Vehicle Tracking Control

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