Title

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.

Meeting Name

AIAA Guidance, Navigation, and Control (GNC) Conference (2013, 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.

Share

 
COinS