System Analysis of a Numerical Predictor-Corrector Aerocapture Guidance Architecture
Abstract
Aerocapture has been envisioned as a potential orbit insertion technique for planetary destinations with an atmosphere. Despite not being flight proven technique, many studies found in the literature and recent mission proposals have employed aerocapture into their respective mission designs. The potential varying levels of trajectory dispersions experienced during atmospheric flight at each destination drives the need for robust and fuel-efficient guidance and control solutions. Existing guidance algorithms have relied on tracking precomputed reference trajectories, which are computed using significant simplifications to the flight mechanics, are not generally designed to be fuel-efficient, and require tedious performance gain tuning. When simulated with higher levels of uncertainty, the existing algorithms have been shown to produce large orbit insertion errors. Furthermore, existing flight control methodologies have been limited in scope to bank angle modulation. While some studies have introduced new methodologies, such as drag modulation and direct force control, they haven’t been tested at the same level of rigor as the existing methods. Advances in on-board computational power are allowing for modern guidance and control solutions, in the form of numerical predictor-corrector algorithms, to be realized. This dissertation presents an aerocapture guidance architecture based on a numerical predictor-corrector algorithm. Optimal control theory is utilized to formulate and numerically obtain fuel-minimizing flight control laws for lifting and ballistic vehicles. The unified control laws are integrated into a common guidance algorithm. The architecture is utilized to conduct Monte Carlo simulation studies of Discovery-class and SmallSat-class aerocapture missions at various planetary destinations.
Degree
Ph.D.
Advisors
Spencer, Purdue University.
Subject Area
Transportation
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