A modified bacterial-foraging tuning algorithm for multimodal optimization of the flight control system

Qi Bian, Brett Nener, Xinmin Wang

Research output: Contribution to journalArticle

Abstract

The flight control parameter tuning of an aircraft is a tedious task even for experienced engineers. In this paper, a Modified Bacterial Foraging Optimization (MBFO) algorithm is developed to deal with control parameter tuning problems and explore the inner relationship between control parameters and the aircraft Flight Control System (FCS). In the proposed method, a serial of bacteria is first assigned to the searching space according to the quasi-Monte Carlo sampling method. Then, the whole colony is divided into several search groups according to the dynamic k-means clustering method. The bacteria are updated according to the nutrient variation in the environment and are always moving towards the areas with more food sources. Moreover, a health assessment strategy is adopted to eliminate bacteria with weak searching abilities and replace them with healthy bacteria to maintain the robust searching ability of the whole colony. Finally, a variety of feasible solutions is illustrated and comparative results are analyzed. A longitudinal FCS of the F/A-18 model designed for aircraft automatic landing is used as a test bed to carry out simulation studies to demonstrate the effectiveness of the proposed method.

Original languageEnglish
Article number105274
Number of pages9
JournalAerospace Science and Technology
DOIs
Publication statusAccepted/In press - 12 Jul 2019

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Flight control systems
Bacteria
Tuning
Aircraft
Landing
Nutrients
Health
Sampling
Engineers

Cite this

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title = "A modified bacterial-foraging tuning algorithm for multimodal optimization of the flight control system",
abstract = "The flight control parameter tuning of an aircraft is a tedious task even for experienced engineers. In this paper, a Modified Bacterial Foraging Optimization (MBFO) algorithm is developed to deal with control parameter tuning problems and explore the inner relationship between control parameters and the aircraft Flight Control System (FCS). In the proposed method, a serial of bacteria is first assigned to the searching space according to the quasi-Monte Carlo sampling method. Then, the whole colony is divided into several search groups according to the dynamic k-means clustering method. The bacteria are updated according to the nutrient variation in the environment and are always moving towards the areas with more food sources. Moreover, a health assessment strategy is adopted to eliminate bacteria with weak searching abilities and replace them with healthy bacteria to maintain the robust searching ability of the whole colony. Finally, a variety of feasible solutions is illustrated and comparative results are analyzed. A longitudinal FCS of the F/A-18 model designed for aircraft automatic landing is used as a test bed to carry out simulation studies to demonstrate the effectiveness of the proposed method.",
keywords = "Bacterial foraging optimization, Control parameter tuning, Multimodal optimization",
author = "Qi Bian and Brett Nener and Xinmin Wang",
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AU - Bian, Qi

AU - Nener, Brett

AU - Wang, Xinmin

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N2 - The flight control parameter tuning of an aircraft is a tedious task even for experienced engineers. In this paper, a Modified Bacterial Foraging Optimization (MBFO) algorithm is developed to deal with control parameter tuning problems and explore the inner relationship between control parameters and the aircraft Flight Control System (FCS). In the proposed method, a serial of bacteria is first assigned to the searching space according to the quasi-Monte Carlo sampling method. Then, the whole colony is divided into several search groups according to the dynamic k-means clustering method. The bacteria are updated according to the nutrient variation in the environment and are always moving towards the areas with more food sources. Moreover, a health assessment strategy is adopted to eliminate bacteria with weak searching abilities and replace them with healthy bacteria to maintain the robust searching ability of the whole colony. Finally, a variety of feasible solutions is illustrated and comparative results are analyzed. A longitudinal FCS of the F/A-18 model designed for aircraft automatic landing is used as a test bed to carry out simulation studies to demonstrate the effectiveness of the proposed method.

AB - The flight control parameter tuning of an aircraft is a tedious task even for experienced engineers. In this paper, a Modified Bacterial Foraging Optimization (MBFO) algorithm is developed to deal with control parameter tuning problems and explore the inner relationship between control parameters and the aircraft Flight Control System (FCS). In the proposed method, a serial of bacteria is first assigned to the searching space according to the quasi-Monte Carlo sampling method. Then, the whole colony is divided into several search groups according to the dynamic k-means clustering method. The bacteria are updated according to the nutrient variation in the environment and are always moving towards the areas with more food sources. Moreover, a health assessment strategy is adopted to eliminate bacteria with weak searching abilities and replace them with healthy bacteria to maintain the robust searching ability of the whole colony. Finally, a variety of feasible solutions is illustrated and comparative results are analyzed. A longitudinal FCS of the F/A-18 model designed for aircraft automatic landing is used as a test bed to carry out simulation studies to demonstrate the effectiveness of the proposed method.

KW - Bacterial foraging optimization

KW - Control parameter tuning

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