An intelligent guidance and control system for ship obstacle avoidance
An intelligent guidance and control system for ship obstacle avoidance
An intelligent guidance and control system using a neurofuzzy network multi-step ahead predictor model is introduced and applied to ship obstacle avoidance, which uses only observed input/output data generated by on-board and external sensors, and a data fusion algorithm to generate the desired way points. A simple and effective way-point guidance scheme based on line of sight is derived for a data-based ship model. A neurofuzzy network predictor, based on using rudder deflection angle for the control of ship heading angle, is utilized on the simulation of an ESSO 190 000 dwt tanker model to demonstrate the effectiveness of the system. The approach is generic and extendable to aircraft and missile control and guidance problems where the vehicle dynamics change significantly during flight in a manner dependent upon operational use, the only requirement for implementation being observed data to construct sensor and vehicle models.
311-320
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Wilson, P.A.
8307fa11-5d5e-47f6-9961-9d43767afa00
August 1999
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Wilson, P.A.
8307fa11-5d5e-47f6-9961-9d43767afa00
Hong, X., Harris, C.J. and Wilson, P.A.
(1999)
An intelligent guidance and control system for ship obstacle avoidance.
Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 213 (4), .
(doi:10.1243/0959651991540179).
Abstract
An intelligent guidance and control system using a neurofuzzy network multi-step ahead predictor model is introduced and applied to ship obstacle avoidance, which uses only observed input/output data generated by on-board and external sensors, and a data fusion algorithm to generate the desired way points. A simple and effective way-point guidance scheme based on line of sight is derived for a data-based ship model. A neurofuzzy network predictor, based on using rudder deflection angle for the control of ship heading angle, is utilized on the simulation of an ESSO 190 000 dwt tanker model to demonstrate the effectiveness of the system. The approach is generic and extendable to aircraft and missile control and guidance problems where the vehicle dynamics change significantly during flight in a manner dependent upon operational use, the only requirement for implementation being observed data to construct sensor and vehicle models.
More information
Published date: August 1999
Organisations:
Fluid Structure Interactions Group
Identifiers
Local EPrints ID: 250661
URI: http://eprints.soton.ac.uk/id/eprint/250661
ISSN: 0959-6518
PURE UUID: 930d510e-6781-4f37-946f-673748d6072b
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Date deposited: 17 Sep 1999
Last modified: 15 Mar 2024 02:35
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Contributors
Author:
X. Hong
Author:
C.J. Harris
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