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

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$585,413.00
Award Year:
2011
Program:
SBIR
Phase:
Phase II
Contract:
NNX11CA84C
Award Id:
n/a
Agency Tracking Number:
095584
Solicitation Year:
2009
Solicitation Topic Code:
O4.04
Solicitation Number:
n/a
Small Business Information
CO, Suite 200, Loveland, CO, 80538-6010
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
956324362
Principal Investigator:
RandyPaffenroth
Principal Investigator
(970) 461-2000
randy.paffenroth@numerica.us
Business Contact:
JeffPoore
Business Official
(970) 461-2000
jeff.poore@numerica.us
Research Institute:
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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