Modeling Aircraft Wing Loads From Flight Data Using Neural Networks

Author : Jeffrey Knog
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (85 download)

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Download or read book The Acccuracy of Parameter Estimation in System Identification of Noisy Aircraft Load Measurement written by Jeffrey Knog and published by . This book was released on 1994 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This thesis focuses on the subject of the accuracy of parameter estimation and system identification techniques. Motivated by a complicated load measurement from NASA Dryden Flight Research Center, advanced system identification techniques are needed. The objective of the problem is to accurately predict the load experienced by the aircraft wing structure during flight determined from a set of calibrated load and gage response relationship. We can then model the problem as a black box input-output system identification from which the system parameter has to be estimated. Traditional LS techniques and the issues of noisy data and model accuracy are addressed. A statistical bound reflecting the change in residual is derived in order to understand the effects of the perturbations on the data. Due to the intrinsic nature of the LS problem, LS solution faces the dilemma of the tradeoff between model accuracy and noise sensitivity. A method of relating the two conflicting performance indices is presented, thus allowing us to improve the noise sensitivity while at the same time confining the degradation of the model accuracy. SVD techniques for data reduction are studied and the equivalence of the Correspondence Analysis (CA) and Total Least Squares Criteria are proved. We also looked at nonlinear LS problems with NASA F-111 data set as an example. Conventional methods are neither easily applicable or suitable for the specific load problem since the exact model of the system is unknown. Neural Network (NN) does not require prior information on the model of the system. This robustness motivated us to apply the NN techniques on our load problem. Simulation results for the NN methods used in both the single load and the "warning signal" problems are both useful and encouraging. The performance of the NN (for single load estimate) is better than the LS approach, whereas no conventional approach was tried for the "warning signals" problem. The NN design methodology is also presented. The use of SVD, CA and Collinearity Index methods are used to reduce the number of neurons in a layer."--Page i.