Robust Optimization of Spacecraft Trajectories using Statistical Thruster Failure Models

Low-thrust propulsion methods are becoming increasingly common in spaceflight missions since they only require a fraction of the propellant mass of chemical propulsion methods. However, the extended thrust arcs required by these methods leave a larger timeframe for missed thrust events to occur which prevent the spacecraft from following its nominal trajectory. If an initial trajectory is not designed to be robust to such losses, missed thrust events may result in the mission being unable to achieve its objectives. Although mass- and time-optimal trajectories have been thoroughly explored, a gap exists in how to directly optimize trajectories for robustness to missed thrust events. This project will study the design of robust, optimal preflight trajectories which take into account statistical models for thruster failure. Improved theoretical statements of the missed thrust analysis problem will be explored. Then, analyses will be performed incorporating a probabilistic thruster failure model. Based on these analyses, I will develop techniques for robust initial trajectory design, as well as improve the process for responsive redesign of trajectories given the realization of a failure. The project objectives will be accomplished by first exploring dynamical systems that produce closed-form solutions in order to develop an understanding of the optimal control problem, incorporate statistical failure models, and define meaningful optimization metrics. I will then progress to advanced interplanetary techniques and finally apply the developed analysis technique to trajectories in cis-lunar space. The research tasks I defined all focus on designing robust nominal trajectories, but the dynamical systems increase in complexity as the research progresses.

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