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Multiobjective gas turbine engine controller design using genetic algorithms

Multiobjective gas turbine engine controller design using genetic algorithms
Multiobjective gas turbine engine controller design using genetic algorithms
This paper describes the use of multiobjective genetic algorithms (MOGAs) in the design of a multivariable control system for a gas turbine engine. The mechanisms employed to facilitate multiobjective search with the genetic algorithm are described with the aid of an example. It is shown that the MOGA confers a number of advantages over conventional multiobjective optimization methods by evolving a family of Pareto-optimal solutions rather than a single solution estimate. This allows the engineer to examine the trade-offs between the different design objectives and configurations during the course of an optimization. In addition, the paper demonstrates how the genetic algorithm can be used to search in both controller structure and parameter space thereby offering a potentially more general approach to optimization in controller design than traditional numerical methods. While the example in the paper deals with control system design, the approach described can be expected to be applicable to more general problems in the fields of computer aided design (CAD) and computer aided engineering (CAE)
0278-0046
583-587
Chipperfield, Andrew
524269cd-5f30-4356-92d4-891c14c09340
Fleming, P.
5d7d2137-53bc-443c-af99-765dc711f636
Chipperfield, Andrew
524269cd-5f30-4356-92d4-891c14c09340
Fleming, P.
5d7d2137-53bc-443c-af99-765dc711f636

Chipperfield, Andrew and Fleming, P. (1996) Multiobjective gas turbine engine controller design using genetic algorithms. IEEE Transactions on Industrial Electronics, 43 (5), 583-587. (doi:10.1109/41.538616).

Record type: Article

Abstract

This paper describes the use of multiobjective genetic algorithms (MOGAs) in the design of a multivariable control system for a gas turbine engine. The mechanisms employed to facilitate multiobjective search with the genetic algorithm are described with the aid of an example. It is shown that the MOGA confers a number of advantages over conventional multiobjective optimization methods by evolving a family of Pareto-optimal solutions rather than a single solution estimate. This allows the engineer to examine the trade-offs between the different design objectives and configurations during the course of an optimization. In addition, the paper demonstrates how the genetic algorithm can be used to search in both controller structure and parameter space thereby offering a potentially more general approach to optimization in controller design than traditional numerical methods. While the example in the paper deals with control system design, the approach described can be expected to be applicable to more general problems in the fields of computer aided design (CAD) and computer aided engineering (CAE)

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Published date: 1 October 1996

Identifiers

Local EPrints ID: 22378
URI: http://eprints.soton.ac.uk/id/eprint/22378
ISSN: 0278-0046
PURE UUID: 97e133bd-8aac-4fb6-b616-707f004bf7c2
ORCID for Andrew Chipperfield: ORCID iD orcid.org/0000-0002-3026-9890

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Date deposited: 30 Jan 2007
Last modified: 16 Mar 2024 03:31

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Author: P. Fleming

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