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Uncertainty optimisation design of USV based on the Six Sigma method

Uncertainty optimisation design of USV based on the Six Sigma method
Uncertainty optimisation design of USV based on the Six Sigma method
In order to improve the sailing performance of unmanned surface vehicles (USVs), maximise the value of their sailing performance function, and minimise disturbances caused by uncertainty factors, this study introduces a 6σ theory of quantitative descriptions of uncertainty levels into the USV sailing performance optimisation model. It uses the Multi-Island Genetic Algorithm to optimise the certainty of the design variables and plot the optimal curve and the Monte Carlo simulation to analyse the sensitivity of the total objective function. The Mean on target and the Minimise variation function are used for the 6σ optimisation. The uncertainty factors are the floating range of the design variables. Results indicate that the design variables affect the value of the total objective function. A comparative analysis of the certainty optimisation results and the results obtained by the 6σ uncertainty optimisation shows that although the value of the latter’s total objective function decreases, its reliability is greatly improved. The 6σ optimisation method, therefore, can be applied to improve the sailing performance design of USVs and obtain reasonable and highly reliable results, which can ensure that the constraint condition has a high degree of immunity to the uncertainty of actual situations.
USV, sailing performance, uncertainity, Monte Carlo, 6σ optimization
0029-8018
1-7
Hou, Yuanhang
f71b3f13-ed85-4867-ad12-3d729acf2a6e
Kang, Kai
c5b44c34-0fb3-4c31-a925-026a2393765c
Xiong, Yeping
51be8714-186e-4d2f-8e03-f44c428a4a49
Sue, Linfang
361dd25f-7b83-4950-92cb-b4314a20080d
Hou, Yuanhang
f71b3f13-ed85-4867-ad12-3d729acf2a6e
Kang, Kai
c5b44c34-0fb3-4c31-a925-026a2393765c
Xiong, Yeping
51be8714-186e-4d2f-8e03-f44c428a4a49
Sue, Linfang
361dd25f-7b83-4950-92cb-b4314a20080d

Hou, Yuanhang, Kang, Kai, Xiong, Yeping and Sue, Linfang (2020) Uncertainty optimisation design of USV based on the Six Sigma method. Ocean Engineering, 2000, 1-7, [107045]. (doi:10.1016/j.oceaneng.2020.107045).

Record type: Article

Abstract

In order to improve the sailing performance of unmanned surface vehicles (USVs), maximise the value of their sailing performance function, and minimise disturbances caused by uncertainty factors, this study introduces a 6σ theory of quantitative descriptions of uncertainty levels into the USV sailing performance optimisation model. It uses the Multi-Island Genetic Algorithm to optimise the certainty of the design variables and plot the optimal curve and the Monte Carlo simulation to analyse the sensitivity of the total objective function. The Mean on target and the Minimise variation function are used for the 6σ optimisation. The uncertainty factors are the floating range of the design variables. Results indicate that the design variables affect the value of the total objective function. A comparative analysis of the certainty optimisation results and the results obtained by the 6σ uncertainty optimisation shows that although the value of the latter’s total objective function decreases, its reliability is greatly improved. The 6σ optimisation method, therefore, can be applied to improve the sailing performance design of USVs and obtain reasonable and highly reliable results, which can ensure that the constraint condition has a high degree of immunity to the uncertainty of actual situations.

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OE accepted paper Hou Xiong 2020 - Accepted Manuscript
Restricted to Repository staff only until 30 January 2022.
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More information

Accepted/In Press date: 30 January 2020
e-pub ahead of print date: 13 February 2020
Published date: 15 March 2020
Keywords: USV, sailing performance, uncertainity, Monte Carlo, 6σ optimization

Identifiers

Local EPrints ID: 438122
URI: http://eprints.soton.ac.uk/id/eprint/438122
ISSN: 0029-8018
PURE UUID: fd493c85-a1f0-47bf-bf17-0dd3da24f33a
ORCID for Yeping Xiong: ORCID iD orcid.org/0000-0002-0135-8464

Catalogue record

Date deposited: 02 Mar 2020 17:30
Last modified: 07 Oct 2020 01:45

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