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Adaptive neural network-based backstepping fault tolerant control for underwater vehicles with thruster fault

Adaptive neural network-based backstepping fault tolerant control for underwater vehicles with thruster fault
Adaptive neural network-based backstepping fault tolerant control for underwater vehicles with thruster fault
A thruster fault tolerant control (FTC) method is developed for underwater vehicles in the presence of modelling uncertainty, external disturbance and unknown thruster fault. The developed method incorporates the sliding mode algorithm and backstepping scheme to improve its robustness to modelling uncertainty and external disturbance. In order to be independent of the fault detection and diagnosis (FDD) unit, thruster fault is treated as a part of the general uncertainty along with the modelling uncertainty and external disturbance, and radial basis function neural network (RBFNN) is adopted to approximate the general uncertainty. According to the Lyapunov theory, control law and adaptive law of RBFNN are derived to ensure the tracking errors asymptotically converge to zero. Trajectory tracking simulations of underwater vehicle subject to modelling uncertainty, ocean currents, tether force and thruster faults are carried out to demonstrate the effectiveness and feasibility of the proposed method.
underwater vehicles, fault tolerant control, backstepping method, neural network, adaptive sliding mode
0029-8018
15-24
Wang, Yujia
8f3f5722-a53e-4061-ba52-60ea723a2273
Wilson, P A
8307fa11-5d5e-47f6-9961-9d43767afa00
Liu, Xing
a48919e5-efd8-405a-a5e2-60508b78a334
Wang, Yujia
8f3f5722-a53e-4061-ba52-60ea723a2273
Wilson, P A
8307fa11-5d5e-47f6-9961-9d43767afa00
Liu, Xing
a48919e5-efd8-405a-a5e2-60508b78a334

Wang, Yujia, Wilson, P A and Liu, Xing (2015) Adaptive neural network-based backstepping fault tolerant control for underwater vehicles with thruster fault. Ocean Engineering, 110 (A), 15-24. (doi:10.1016/j.oceaneng.2015.09.035).

Record type: Article

Abstract

A thruster fault tolerant control (FTC) method is developed for underwater vehicles in the presence of modelling uncertainty, external disturbance and unknown thruster fault. The developed method incorporates the sliding mode algorithm and backstepping scheme to improve its robustness to modelling uncertainty and external disturbance. In order to be independent of the fault detection and diagnosis (FDD) unit, thruster fault is treated as a part of the general uncertainty along with the modelling uncertainty and external disturbance, and radial basis function neural network (RBFNN) is adopted to approximate the general uncertainty. According to the Lyapunov theory, control law and adaptive law of RBFNN are derived to ensure the tracking errors asymptotically converge to zero. Trajectory tracking simulations of underwater vehicle subject to modelling uncertainty, ocean currents, tether force and thruster faults are carried out to demonstrate the effectiveness and feasibility of the proposed method.

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More information

Accepted/In Press date: 16 September 2015
e-pub ahead of print date: 26 October 2015
Published date: 1 December 2015
Keywords: underwater vehicles, fault tolerant control, backstepping method, neural network, adaptive sliding mode
Organisations: Fluid Structure Interactions Group

Identifiers

Local EPrints ID: 383437
URI: http://eprints.soton.ac.uk/id/eprint/383437
ISSN: 0029-8018
PURE UUID: 643e4c02-c966-45cd-b8fb-5748b354ce12
ORCID for P A Wilson: ORCID iD orcid.org/0000-0002-6939-682X

Catalogue record

Date deposited: 02 Nov 2015 16:44
Last modified: 27 Apr 2022 01:32

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Contributors

Author: Yujia Wang
Author: P A Wilson ORCID iD
Author: Xing Liu

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