|Table of Contents|

Aircraft self-organization algorithm with redundant trend

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2014年05期
Page:
602-607
Research Field:
Publishing date:

Info

Title:
Aircraft self-organization algorithm with redundant trend
Author(s):
Neusypin K A1Proletarsky A V1Shen Kai12Liu Rongzhong2Guo Rui2
1.Department of Informatics and Control Systems,Bauman Moscow State Technical University, Moscow 105005,Russia; 2.School of Mechanical Engineering,NUST,Nanjing 210094,China
Keywords:
aircrafts navigation system self-organization algorithm redundant trends self-organization selection criteria
PACS:
V249.32; TP273
DOI:
-
Abstract:
In order to improve the accuracy of aircraft navigation systems,one error compensating model for autonomous navigation systems is established based upon inertial navigation systems by applying the Kalman filtering algorithm and other prediction algorithms.In the case of the absence of partial measurement information,linear and harmonic functions are proposed to be selected as basic functions according to characteristics of mathematic error models of navigation systems.A novel adaptive self-organization measuring complex is built on the basis of self-organization algorithm with redundant trends by utilizing some self-organization selection criteria.The mathematic simulation and real test based on actual navigation testing systems are executed.The analysis results show that self-organization algorithm with redundant trends can secure the higher prediction accuracy of navigation system errors and meet real-time requirements.

References:

[1] Neusypin K A,Proletarsky A V,Vlasov S V.Accuracy increase methods of the autonomous navigation systems[J].Automation and Modern Technologies,2011(2):14-18.
[2]Proletarsky A V,Neusypin K A.Methods for correction of navigation systems and complexes of flying vehicles[J].Journal of Bauman Moscow State Technical University,2012(5):216-223.
[3]Neusypin K A,Proletarsky A V,Vlasov S V.The accuracy improvement of self-organizing automation system[J].Instrumentation and Control Systems,2010,3:68-74.
[4]Proletarsky A V,Neusipin K A,Fang Ke.The methods of compensating navigation system of unmanned aircraft[J].Automation and Modern Technology,2013(2):30-34.
[5]Brown R G.Integrated navigation systems and Kalman filtering:a perspective[J].Navigation,1972-1973,19(4):355-362.
[6]Koifman M,Bar-ltzhack I,Mernav S.Dynamics aided inertial navigation system[R].AIAA-95-3195-CP.
[7]Carvalho H,Moral P D,Salut G.Optimal nonlinear filtering in GPS/INS integration[J].IEEE Trans on Aerospace and Electronic Systems,1997,33(3):835-850.
[8]Ivakhnenko A G,Koppa Yu V,Min W S.Polynomial and logical theory of dynamic systems(pt.1)[J].Sov Automat Contr,1970,3(3):1-13.
[9]Ivakhnenko A G.Polynomial theory of complex systems[J].IEEE Trans on Systems,Man and Cybernetics,1971,SMC1(4):364-378.
[10]Neusipin K A,Fang Ke.The research on modeling algorithm using self-organization method in aircraft intelligent control system[J].Journal of Projectiles Rocket Missiles and Guidance,2010(5):39-42.
[11]Duffy J J,Franklin M A.A learning identification algorithm and its application to environmental systems[J].IEEE Transactions on Systems,Man and Cybernetics,1975,SMC5(5):226-240.
[12] Ikeda S M,Ochial M,Sawaragi Y.Sequential GMDH algorithm and its application to river flow prediction[J].IEEE Transactions on Systems,Man and Cybernetics,1976,SMC6(7):473-479.
[13]Sawaragi Y,Soelda T,Tamura H.Statistical prediction of air pollution levels using non-physical models[J].Automatica,1979,15(4):441-451.
[14]Fang Ke,Proletarsky A V,Neusipin K A.Selection of measured signals in the navigation measuring complex[J].Journal of Measurement Science and Instrumenta-tion,2011,2:346-348.
[15]Neusypin K A,Proletarsky A V.Development of a reduced self-organization algorithm for navigation systems correction[J].Scientific Review,2013(9):333-337.
[16]Neusypin K A,Proletarskiy A V,Vays Yu L,et al.Selection criteria ensemble formation of the self-organization compact algorithm[J].Automation and Modern Technologies,2012(4):14-16.
[17]Neusypin K A,Shen Kai.Modification of nonlinear Kalman filtering by utilizing genetic algorithms[J].Automation and Modern Technologies,2014(5):9-11.

Memo

Memo:
-
Last Update: 2014-10-31