Estimation of longitudinal aerodynamic parameters using recurrent neural network

Author:

Verma H. O.ORCID,Peyada N. K.

Abstract

AbstractThe aerodynamic modelling is one of the challenging tasks that is generally established using the results of the computational fluid dynamic software and wind tunnel analysis performed either on the scaled model or the prototype. In order to improve the confidence of the estimates, the conventional parameter estimation methods such as equation error method (EEM) and output error method (OEM) are more often applied to extract the aircraft’s stability and control derivatives from its respective flight test data. The quality of the estimates gets influenced due to the presence of the measurement and process noises in the flight test data. With the advancement in the machine learning algorithms, the data driven methods have got more attention in the modelling of a system based on the input-output measurements and also, in the identification of the system/model parameters. The research article investigates the longitudinal stability and control derivatives of the aerodynamic models by using an integrated optimisation algorithm based on a recurrent neural network. The flight test data of Hansa-3 and HFB 320 aircraft were used as case studies to see the efficacy of the parameter estimation algorithm and further, the confidence of the estimates were demonstrated in terms of the standard deviations. Finally, the simulated variables obtained using the estimates demonstrate a qualitative estimation in the presence of the noise.

Publisher

Cambridge University Press (CUP)

Subject

Aerospace Engineering

Reference37 articles.

1. Evaluation of Recursive Least Squares algorithm for parameter estimation in aircraft real time applications

2. Aircraft parameter estimation using hybrid neuro fuzzy and artificial bee colony optimization (HNFABC) algorithm;Roy;J. Aerosp. Sci. Technol.,2017

3. Application of System Identification to Aircraft at NASA Langley Research Center

4. Parameter estimation of stable and unstable aircraft using extreme learning machine;Verma;AIAA 2018-0526,2018

5. [35] Rangaranjan, R. and Vishwanathan, S. Wind Tunnel Test Results on a 1/5 Scale HANSA Model. NAL TR-01 1997.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3