Authors' Class Standing

Juan F Gutierrez, Junior David Gomez Herrera, Junior

Lead Presenter

David Herrera-Gomez

Faculty Mentor Name

Bradley Wall

Format Preference

Poster

Abstract

As we advance into a modern space-age there is a need to obtain better trajectories for spacecraft missions, in which optimization plays an integral role. These improved trajectories will provide a more cost efficient means of conducting space missions. Throughout this paper such optimization will be discussed in terms of accomplishment and potential problems.

Previous trajectory optimization research efforts include using the idea that the shape-based solution is only near-optimal and as a result there should exist a neighboring solution with a lower cost. Using Linear Quadratic Regulator (LQR) control and linearized equations about the nominal shape-based trajectory, a shape-smoothing algorithm is built that improves the cost of the trajectory. This LQR algorithm also addresses the 2 drawbacks. First, the LQR removes the constraint that the in-plane thrust vector be parallel to the velocity vector. This improves the thrust pointing angle guess for any further optimization method. Second, since the overall thrust level does typically (but is not guaranteed to) decrease, the initial thrust acceleration for the LQR trajectory is lower than that of the shape-based method. This helps (but still does not guarantee that) the initial thrust acceleration for the LQR trajectory to be equal to or less than what the physical hardware can provide.

As a continuation of previous research the LQR optimization method was used for multi-body target trajectory. Multiple examples were created ranging from one body to three body trajectories in order to verify the accuracy of the method compared to others that have been used.

Start Date

4-4-2014 12:00 PM

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Apr 4th, 12:00 PM

Spacecraft Low Thrust Propulsion Optimization System

As we advance into a modern space-age there is a need to obtain better trajectories for spacecraft missions, in which optimization plays an integral role. These improved trajectories will provide a more cost efficient means of conducting space missions. Throughout this paper such optimization will be discussed in terms of accomplishment and potential problems.

Previous trajectory optimization research efforts include using the idea that the shape-based solution is only near-optimal and as a result there should exist a neighboring solution with a lower cost. Using Linear Quadratic Regulator (LQR) control and linearized equations about the nominal shape-based trajectory, a shape-smoothing algorithm is built that improves the cost of the trajectory. This LQR algorithm also addresses the 2 drawbacks. First, the LQR removes the constraint that the in-plane thrust vector be parallel to the velocity vector. This improves the thrust pointing angle guess for any further optimization method. Second, since the overall thrust level does typically (but is not guaranteed to) decrease, the initial thrust acceleration for the LQR trajectory is lower than that of the shape-based method. This helps (but still does not guarantee that) the initial thrust acceleration for the LQR trajectory to be equal to or less than what the physical hardware can provide.

As a continuation of previous research the LQR optimization method was used for multi-body target trajectory. Multiple examples were created ranging from one body to three body trajectories in order to verify the accuracy of the method compared to others that have been used.