Multidisciplinary design optimisation of Unmanned Aerial Systems (UAS) using Meta model Assisted Evolutionary Algorithms

L. F. Gonzalez; R. Walker; K. Srinivas; J. Periaux

Multidisciplinary design optimisation of Unmanned Aerial Systems (UAS) using Meta model Assisted Evolutionary Algorithms

L. F. Gonzalez^*, R. Walker, K. Srinivas, J. Periaux

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

2 Citations (Scopus)

Abstract

Unmanned Aerial Systems (UAS) is recognised to be the next revolution in aviation as information technology matures in the aerospace sector. UAS systems are multidiscipline systems as they integrate several disciplines, e.g. avionics, flight control, aerodynamics, structures. The design and optimisation of these vehicles can be multi-modal, non-convex or discontinuous, with multiple local minima and with noise. Traditional gradient based optimisation method might fail to find true optimal solutions or Pareto Fronts. This paper explores the design and coupling of Meta-model Assisted (MMA) with Multi-Objective Evolutionary Algorithms (MOEA) for Unmanned Aerial Systems (UAS) design. Results indicate an improvement on optimisation performance and both practicality and robustness of the method in finding optimal solutions and Pareto trade-offs between the disciplines.

Original language	English
Title of host publication	Proceedings of the 16th Australasian Fluid Mechanics Conference, 16AFMC
Editors	Peter Jacobs, Tim McIntyre, Matthew Cleary, David Buttsworth, David Mee, Rose Clements, Richard Morgan, Charles Lemckert
Place of Publication	St. Lucia, Qld
Publisher	School of Engineering, University of Queensland
Pages	471-474
Number of pages	4
ISBN (Print)	9781864998948
Publication status	Published - 2007
Event	16th Australasian Fluid Mechanics Conference, 16AFMC - Gold Coast, QLD, Australia Duration: 3 Dec 2007 → 7 Dec 2007

Other

Other	16th Australasian Fluid Mechanics Conference, 16AFMC
Country/Territory	Australia
City	Gold Coast, QLD
Period	3/12/07 → 7/12/07

Cite this

Gonzalez, L. F., Walker, R., Srinivas, K., & Periaux, J. (2007). Multidisciplinary design optimisation of Unmanned Aerial Systems (UAS) using Meta model Assisted Evolutionary Algorithms. In P. Jacobs, T. McIntyre, M. Cleary, D. Buttsworth, D. Mee, R. Clements, R. Morgan, & C. Lemckert (Eds.), Proceedings of the 16th Australasian Fluid Mechanics Conference, 16AFMC (pp. 471-474). School of Engineering, University of Queensland.

Gonzalez, L. F. ; Walker, R. ; Srinivas, K. et al. / Multidisciplinary design optimisation of Unmanned Aerial Systems (UAS) using Meta model Assisted Evolutionary Algorithms. Proceedings of the 16th Australasian Fluid Mechanics Conference, 16AFMC. editor / Peter Jacobs ; Tim McIntyre ; Matthew Cleary ; David Buttsworth ; David Mee ; Rose Clements ; Richard Morgan ; Charles Lemckert. St. Lucia, Qld : School of Engineering, University of Queensland, 2007. pp. 471-474

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title = "Multidisciplinary design optimisation of Unmanned Aerial Systems (UAS) using Meta model Assisted Evolutionary Algorithms",

abstract = "Unmanned Aerial Systems (UAS) is recognised to be the next revolution in aviation as information technology matures in the aerospace sector. UAS systems are multidiscipline systems as they integrate several disciplines, e.g. avionics, flight control, aerodynamics, structures. The design and optimisation of these vehicles can be multi-modal, non-convex or discontinuous, with multiple local minima and with noise. Traditional gradient based optimisation method might fail to find true optimal solutions or Pareto Fronts. This paper explores the design and coupling of Meta-model Assisted (MMA) with Multi-Objective Evolutionary Algorithms (MOEA) for Unmanned Aerial Systems (UAS) design. Results indicate an improvement on optimisation performance and both practicality and robustness of the method in finding optimal solutions and Pareto trade-offs between the disciplines.",

author = "Gonzalez, {L. F.} and R. Walker and K. Srinivas and J. Periaux",

year = "2007",

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editor = "Peter Jacobs and Tim McIntyre and Matthew Cleary and David Buttsworth and Mee, { David} and Rose Clements and Richard Morgan and Charles Lemckert",

booktitle = "Proceedings of the 16th Australasian Fluid Mechanics Conference, 16AFMC",

publisher = "School of Engineering, University of Queensland",

note = "16th Australasian Fluid Mechanics Conference, 16AFMC ; Conference date: 03-12-2007 Through 07-12-2007",

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Gonzalez, LF, Walker, R, Srinivas, K & Periaux, J 2007, Multidisciplinary design optimisation of Unmanned Aerial Systems (UAS) using Meta model Assisted Evolutionary Algorithms. in P Jacobs, T McIntyre, M Cleary, D Buttsworth, D Mee, R Clements, R Morgan & C Lemckert (eds), Proceedings of the 16th Australasian Fluid Mechanics Conference, 16AFMC. School of Engineering, University of Queensland, St. Lucia, Qld, pp. 471-474, 16th Australasian Fluid Mechanics Conference, 16AFMC, Gold Coast, QLD, Australia, 3/12/07.

Multidisciplinary design optimisation of Unmanned Aerial Systems (UAS) using Meta model Assisted Evolutionary Algorithms. / Gonzalez, L. F.; Walker, R.; Srinivas, K. et al.
Proceedings of the 16th Australasian Fluid Mechanics Conference, 16AFMC. ed. / Peter Jacobs; Tim McIntyre; Matthew Cleary; David Buttsworth; David Mee; Rose Clements; Richard Morgan; Charles Lemckert. St. Lucia, Qld: School of Engineering, University of Queensland, 2007. p. 471-474.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AB - Unmanned Aerial Systems (UAS) is recognised to be the next revolution in aviation as information technology matures in the aerospace sector. UAS systems are multidiscipline systems as they integrate several disciplines, e.g. avionics, flight control, aerodynamics, structures. The design and optimisation of these vehicles can be multi-modal, non-convex or discontinuous, with multiple local minima and with noise. Traditional gradient based optimisation method might fail to find true optimal solutions or Pareto Fronts. This paper explores the design and coupling of Meta-model Assisted (MMA) with Multi-Objective Evolutionary Algorithms (MOEA) for Unmanned Aerial Systems (UAS) design. Results indicate an improvement on optimisation performance and both practicality and robustness of the method in finding optimal solutions and Pareto trade-offs between the disciplines.

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ER -

Gonzalez LF, Walker R, Srinivas K, Periaux J. Multidisciplinary design optimisation of Unmanned Aerial Systems (UAS) using Meta model Assisted Evolutionary Algorithms. In Jacobs P, McIntyre T, Cleary M, Buttsworth D, Mee D, Clements R, Morgan R, Lemckert C, editors, Proceedings of the 16th Australasian Fluid Mechanics Conference, 16AFMC. St. Lucia, Qld: School of Engineering, University of Queensland. 2007. p. 471-474

Multidisciplinary design optimisation of Unmanned Aerial Systems (UAS) using Meta model Assisted Evolutionary Algorithms

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