A survey of AI techniques for control of underwater vehicles

Pepijn Van De Ven; Colin Flanagan; Daniel Toal

doi:10.1016/S1474-6670(17)36672-7

A survey of AI techniques for control of underwater vehicles

Pepijn Van De Ven, Colin Flanagan, Daniel Toal

Department of Electronics & Computer Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper presents a review of recent research efforts in the field of the application of neural and fuzzy networks in the control of unmanned underwater vehicles (UUV). A classification for UUV control architectures using AI techniques is presented and consecutively used to categorise the approaches found in the literature. Several projects are discussed in detail and each control strategy is categorized as per the presented framework. Based on practical results from those projects, as reported in the literature, a cptalitative assessment regarding the performance of the control strategies is given. Their advantages and disadvantages are identified and discussed. Based on the authors7 observations, possible future trends are identified.

Original language	English
Pages (from-to)	145-150
Number of pages	6
Journal	IFAC Proceedings Volumes (IFAC-PapersOnline)
Volume	36
Issue number	4
DOIs	https://doi.org/10.1016/S1474-6670(17)36672-7
Publication status	Published - 2003
Event	1st IFAC Workshop on Guidance and Control of Underwater Vehicles, GCUV 2003 - Newport, United Kingdom Duration: 9 Apr 2003 → 11 Apr 2003

Keywords

Artificial intelligence
Attitude control
Autonomous control
Autonomous vehicles
Fuzzy control
Marine systems
Model based control
Neural control
Neural networks
Nonlinear control

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10.1016/S1474-6670(17)36672-7

Cite this

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title = "A survey of AI techniques for control of underwater vehicles",

abstract = "This paper presents a review of recent research efforts in the field of the application of neural and fuzzy networks in the control of unmanned underwater vehicles (UUV). A classification for UUV control architectures using AI techniques is presented and consecutively used to categorise the approaches found in the literature. Several projects are discussed in detail and each control strategy is categorized as per the presented framework. Based on practical results from those projects, as reported in the literature, a cptalitative assessment regarding the performance of the control strategies is given. Their advantages and disadvantages are identified and discussed. Based on the authors7 observations, possible future trends are identified.",

keywords = "Artificial intelligence, Attitude control, Autonomous control, Autonomous vehicles, Fuzzy control, Marine systems, Model based control, Neural control, Neural networks, Nonlinear control",

author = "{Van De Ven}, Pepijn and Colin Flanagan and Daniel Toal",

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AU - Van De Ven, Pepijn

AU - Flanagan, Colin

AU - Toal, Daniel

PY - 2003

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AB - This paper presents a review of recent research efforts in the field of the application of neural and fuzzy networks in the control of unmanned underwater vehicles (UUV). A classification for UUV control architectures using AI techniques is presented and consecutively used to categorise the approaches found in the literature. Several projects are discussed in detail and each control strategy is categorized as per the presented framework. Based on practical results from those projects, as reported in the literature, a cptalitative assessment regarding the performance of the control strategies is given. Their advantages and disadvantages are identified and discussed. Based on the authors7 observations, possible future trends are identified.

KW - Artificial intelligence

KW - Attitude control

KW - Autonomous control

KW - Autonomous vehicles

KW - Fuzzy control

KW - Marine systems

KW - Model based control

KW - Neural control

KW - Neural networks

KW - Nonlinear control

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VL - 36

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EP - 150

JO - IFAC Proceedings Volumes (IFAC-PapersOnline)

JF - IFAC Proceedings Volumes (IFAC-PapersOnline)

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T2 - 1st IFAC Workshop on Guidance and Control of Underwater Vehicles, GCUV 2003

Y2 - 9 April 2003 through 11 April 2003

ER -

A survey of AI techniques for control of underwater vehicles

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Keywords

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