TY - GEN

T1 - A gaussian function network for uncertainty propagation through nonlinear dynamical system

AU - Singla, Puneet

AU - Singh, Tarunraj

N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.

PY - 2008

Y1 - 2008

N2 - A Gaussian mixture model approach is proposed for accurate uncertainty propagation through a general nonlinear system. The transition probability density function, is approximated by a finite sum of Gaussian density functions whose parameters (mean and covariance) are propagated using linear propagation theory. Further, Fokker-Planck equation error is used as a feedback to adapt for the amplitude of different Gaussian components while solving a convex quadratic programming problem. The proposed method is applied to a variety of test problems in the open literature, and argued to be an excellent candidate for higher dimensional uncertainty propagation problems.

AB - A Gaussian mixture model approach is proposed for accurate uncertainty propagation through a general nonlinear system. The transition probability density function, is approximated by a finite sum of Gaussian density functions whose parameters (mean and covariance) are propagated using linear propagation theory. Further, Fokker-Planck equation error is used as a feedback to adapt for the amplitude of different Gaussian components while solving a convex quadratic programming problem. The proposed method is applied to a variety of test problems in the open literature, and argued to be an excellent candidate for higher dimensional uncertainty propagation problems.

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M3 - Conference contribution

AN - SCOPUS:60349127032

SN - 9780877035442

T3 - Advances in the Astronautical Sciences

SP - 851

EP - 864

BT - Space Flight Mechanics 2008 - Advances in the Astronautical Sciences, Proceedings of the AAS/AIAA Space Flight Mechanics Meeting

T2 - 18th Annual Space Flight Mechanics Meeting

Y2 - 27 January 2008 through 31 January 2008

ER -