Sigma Point Modified State Observer for Nonlinear Uncertainty Estimation
Abstract
A neural network based novel state observer, known as the Sigma Point Modified State Observer, is presented. The Sigma Point Modified State Observer uses sigma point filtering techniques, similar to the Unscented Kalman Filter, in combination with a neural network to estimate system states, state error covariance, and system uncertainty in nonlinear systems online. Spacecraft atmospheric reentry simulation results are presented to show the validity of the Sigma Point Modified State Observer to highly nonlinear systems with significant uncertainty.
Recommended Citation
J. E. Darling et al., "Sigma Point Modified State Observer for Nonlinear Uncertainty Estimation," Proceedings of the AIAA Guidance, Navigation, and Control Conference (2013, Boston, MA), American Institute of Aeronautics and Astronautics (AIAA), Aug 2013.
The definitive version is available at https://doi.org/10.2514/6.2013-4866
Meeting Name
AIAA Guidance, Navigation, and Control (GNC) Conference (2013: Aug. 19-22, Boston, MA)
Department(s)
Mechanical and Aerospace Engineering
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2013 American Institute of Aeronautics and Astronautics (AIAA), All rights reserved.
Publication Date
22 Aug 2013
