Continuation Methods and Non-Linear/Non-Gaussian Estimation for Flight Dynamics

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNX11CA84C
Agency Tracking Number: 095584
Amount: $585,413.00
Phase: Phase II
Program: SBIR
Awards Year: 2011
Solicitation Year: 2009
Solicitation Topic Code: O4.04
Solicitation Number: N/A
Small Business Information
Numerica Corporation
CO, Suite 200, Loveland, CO, 80538-6010
DUNS: 956324362
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Randy Paffenroth
 Principal Investigator
 (970) 461-2000
 randy.paffenroth@numerica.us
Business Contact
 Jeff Poore
Title: Business Official
Phone: (970) 461-2000
Email: jeff.poore@numerica.us
Research Institution
 Stub
Abstract
We propose herein to augment current NASA spaceflight dynamics programs with algorithms and software from three domains. First, we use parameter continuation methods to assist in computation of trajectories in complicated dynamical situations. Numerical parameter continuation methods have been used extensively to compute a menagerie of structures in dynamical systems including fixed points, periodic orbits, simple bifurcations and invariant manifolds. Perhaps more important for the current work, such methods have already proven their worth in flight dynamics problems, especially those having to do with the complicated dynamics near libration points. Second, we propose to use Continuous Mechanics and Optimal Control (CMOC). Algorithms based the CMOC formalism promises to support optimal trajectory design using both discrete and continuous control. Third, we propose to use advanced filtering techniques and representations of probability density functions to appropriately compute and manage the uncertainty in the trajectories. While advanced methods for understanding and leveraging the underlying dynamics are clearly necessary for effective mission design, planning, and analysis, we contend that they do not suffice. In particular, they do not, in and of themselves, address the issue of uncertainty.Herein we discuss methods that balance the accuracy of the uncertainty representation against computational tractability.

* information listed above is at the time of submission.

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