Data‐driven approach for estimating longitudinal aerodynamic parameters using neural artificial bee colony fusion algorithm

Aerodynamic parameter estimation involves modeling both force and moment coefficients along with the computation of stability and control derivatives from recorded flight data. Classical methods like output, filter, and equation errors apply extensively to this problem. Machine learning approaches l...

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Bibliographic Details
Published in:Asian Journal of Control Vol. 27; no. 1; pp. 76 - 98
Main Authors: Kumar, Prashant, Sonkar, Sarvesh Kumar, George, Riya Catherine, Philip, Deepu, Ghosh, Ajoy Kanti
Format: Journal Article
Language:English
Japanese
Published: Hoboken Wiley 01.01.2025
Wiley Subscription Services, Inc
Subjects:
aerodynamic parameter estimation
Aerodynamic stability
Aircraft
Aircraft control
Aircraft noise
Algorithms
artificial bee colony optimization
artificial intelligence
Artificial neural networks
Control stability
Error analysis
Kinematics
Machine learning
neural networks
Noise intensity
Noise measurement
Optimization
Optimization techniques
Parameter estimation
Parameter identification
Parameter uncertainty
Swarm intelligence
System identification
ISSN:1561-8625, 1934-6093
Online Access:Get full text
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Summary:Aerodynamic parameter estimation involves modeling both force and moment coefficients along with the computation of stability and control derivatives from recorded flight data. Classical methods like output, filter, and equation errors apply extensively to this problem. Machine learning approaches like artificial neural networks (ANNs) provide an alternative to model‐based methods. This work presents a novel aerodynamic parameters estimation technique involving the fusion of two of the most popular machine learning methods. The process uses biologically inspired optimization techniques, the artificial bee colony (ABC) optimization with the widely used ANN for simulated data contaminated with the noise of varying intensity (5%, 10%) and for real aircraft while considering system and measurement uncertainty. Combining ABC and ANN results in a novel and promising method that can address the sensor noise challenges of system identification and parameter estimation. Comparing the proposed approach's results with other benchmark estimation techniques like Least Square and Filter Error Methods established its efficacy. Furthermore, the feasibility of the proposed hybrid method in extracting the stability and control variables from the stable aircraft kinematics is shown even with insufficient information in its data history.
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ISSN:1561-8625
1934-6093
DOI:10.1002/asjc.3164