Deterministic versus evolutionary optimisation methods for nonlinear robustness analysis of flight control laws

Prathyush Menon; Declan Bates; Ian Postlethwaite

doi:10.1109/CEC.2007.4424707

Deterministic versus evolutionary optimisation methods for nonlinear robustness analysis of flight control laws

Prathyush Menon, Declan Bates, Ian Postlethwaite

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

3 Citations (Scopus)

Abstract

This paper considers the application of two different global optimisation approaches to the problem of analysing the robustness (flight clearance) of nonlinear flight control systems. The analysis employs a typical nonlinear clearance criterion used by the European aerospace industry together with a detailed simulation model of a high performance aircraft with a full authority control law. The deterministic optimisation algorithm used in the study is Dividing RECTangles (DIRECT), while the evolutionary algorithm is Differential Evolution. Both algorithms are hybridised with local gradient- based optimisation methods to improve convergence rates near the global solution. The reliability, computational complexity and efficiency of the two approaches are compared for this realistic engineering example, and the prospects for application of optimisation-based methods in the industrial flight clearance process are discussed.

Original language	English
Title of host publication	2007 IEEE Congress on Evolutionary Computation
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1910-1917
ISBN (Print)	978-1424413393
DOIs	https://doi.org/10.1109/CEC.2007.4424707
Publication status	Published - 2007
Event	IEEE Congress on Evolutionary Computation, 2007. CEC 2007 - Singapore Duration: 1 Jan 2007 → …

Conference

Conference	IEEE Congress on Evolutionary Computation, 2007. CEC 2007
Period	1/01/07 → …

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10.1109/CEC.2007.4424707

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title = "Deterministic versus evolutionary optimisation methods for nonlinear robustness analysis of flight control laws",

abstract = "This paper considers the application of two different global optimisation approaches to the problem of analysing the robustness (flight clearance) of nonlinear flight control systems. The analysis employs a typical nonlinear clearance criterion used by the European aerospace industry together with a detailed simulation model of a high performance aircraft with a full authority control law. The deterministic optimisation algorithm used in the study is Dividing RECTangles (DIRECT), while the evolutionary algorithm is Differential Evolution. Both algorithms are hybridised with local gradient- based optimisation methods to improve convergence rates near the global solution. The reliability, computational complexity and efficiency of the two approaches are compared for this realistic engineering example, and the prospects for application of optimisation-based methods in the industrial flight clearance process are discussed.",

author = "Prathyush Menon and Declan Bates and Ian Postlethwaite",

note = "Proceedings of CEC 2007:IEEE Congress on Evolutionary Computation, 2007, held in Singapore from 25-28 September 2007.; IEEE Congress on Evolutionary Computation, 2007. CEC 2007 ; Conference date: 01-01-2007",

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T1 - Deterministic versus evolutionary optimisation methods for nonlinear robustness analysis of flight control laws

AU - Menon, Prathyush

AU - Bates, Declan

AU - Postlethwaite, Ian

N1 - Proceedings of CEC 2007:IEEE Congress on Evolutionary Computation, 2007, held in Singapore from 25-28 September 2007.

PY - 2007

Y1 - 2007

N2 - This paper considers the application of two different global optimisation approaches to the problem of analysing the robustness (flight clearance) of nonlinear flight control systems. The analysis employs a typical nonlinear clearance criterion used by the European aerospace industry together with a detailed simulation model of a high performance aircraft with a full authority control law. The deterministic optimisation algorithm used in the study is Dividing RECTangles (DIRECT), while the evolutionary algorithm is Differential Evolution. Both algorithms are hybridised with local gradient- based optimisation methods to improve convergence rates near the global solution. The reliability, computational complexity and efficiency of the two approaches are compared for this realistic engineering example, and the prospects for application of optimisation-based methods in the industrial flight clearance process are discussed.

AB - This paper considers the application of two different global optimisation approaches to the problem of analysing the robustness (flight clearance) of nonlinear flight control systems. The analysis employs a typical nonlinear clearance criterion used by the European aerospace industry together with a detailed simulation model of a high performance aircraft with a full authority control law. The deterministic optimisation algorithm used in the study is Dividing RECTangles (DIRECT), while the evolutionary algorithm is Differential Evolution. Both algorithms are hybridised with local gradient- based optimisation methods to improve convergence rates near the global solution. The reliability, computational complexity and efficiency of the two approaches are compared for this realistic engineering example, and the prospects for application of optimisation-based methods in the industrial flight clearance process are discussed.

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DO - 10.1109/CEC.2007.4424707

M3 - Chapter

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EP - 1917

BT - 2007 IEEE Congress on Evolutionary Computation

PB - IEEE

CY - Piscataway, NJ

T2 - IEEE Congress on Evolutionary Computation, 2007. CEC 2007

Y2 - 1 January 2007

ER -

Deterministic versus evolutionary optimisation methods for nonlinear robustness analysis of flight control laws

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