Use of evolutionary computation techniques for exploration and prediction of helicopter loads

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Proceedings titleIEEE 2012 World Congress on Computational Intelligence, IEEE 2012 Congress on Evolutionary Computation
ConferenceIEEE 2012 Congress on Evolutionary Computation CEC 2012, June 10-15, 2012, Brisbane, Australia
Pages18; # of pages: 8
AbstractThe development of accurate load spectra for helicopters is necessary for life cycle management and life extension efforts. This paper explores continued efforts to utilize evolutionary computation (EC) methods and machine learning techniques to estimate several helicopter dynamic loads. Estimates for the main rotor normal bending (MRNBX) on the Australian Black Hawk helicopter were generated from an input set that included thirty standard flight state and control system parameters under several flight conditions (full speed forward level flight, rolling left pullout at 1:5g, and steady 45◦ left turn at full speed). Multiobjective genetic algorithms (MOGA) used in combination with the Gamma test found reduced subsets of predictor variables with modeling potential. These subsets were used to estimate MRNBX using Cartesian genetic programming and neural network models trained by deterministic and evolutionary computation techniques, including particle swarm optimization (PSO), differential evolution (DE), and MOGA. PSO and DE were used alone or in combination with deterministic methods. Different error measures were explored including a fuzzy-based asymmetric error function. EC techniques played an important role in both the exploratory and modeling phase of the investigation. The results of this work show that the addition of EC techniques in the modeling stage generated more accurate and correlated models than could be obtained using only deterministic optimization.
Publication date
AffiliationNRC Institute for Aerospace Research; NRC Institute for Information Technology; National Research Council Canada
Peer reviewedYes
NPARC number20833318
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Record identifierce893984-d691-4e9c-9609-908b719ce15e
Record created2012-10-22
Record modified2016-05-09
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