Aerospace guidance problems are characterized by significant non-linearities which make trajectories difficult to compute, and typically require the vehicle to closely follow the reference…

This federal contract award, valued at $599,111.00, was granted by the National Aeronautics and Space Administration (NASA) to The Regents of the University of Colorado, doing business as the University of Colorado Office of Contracts and Grants Division. The contract is for research and development to improve the predictor-corrector algorithms used in entry, descent, and landing (EDL) guidance systems. The work aims to address uncertainties in system dynamics and vehicle control that can lead to targeting errors, by developing enhanced methods for the prediction and control aspects of these algorithms. The contract is a grant for research and does not have a set-aside designation. The University of Colorado is a U.S. state government entity and accredited public research institution with expertise in science, technology, engineering, and mathematics disciplines applicable to aerospace and defense applications.

Aerospace guidance problems are characterized by significant non-linearities which make trajectories difficult to compute, and typically require the vehicle to closely follow the reference trajectory in order for the guidance system to remain valid. The most common way to approach these guidance problems is through the use of a predictor-corrector method. While highly successful to date, the performance is still limited by the assumptions built into the algorithms in use. One major source of error that remains is due to inaccurate prediction steps due to uncertainties in the system dynamics. For EDL problems, there are significant uncertainties in the environment due to atmospheric conditions, as well as in the vehicle control due to modeling uncertainties. Even if the prediction problem is addressed, the control still must be capable of finding a valid solution to null any targeting errors. This proposal will recommend methods for improving both aspects of the predictor-corrector algorithm for EDL guidance applications based on advances in understanding of the system dynamics and control methods.