PARAMETRIC OPTIMIZATION AND GUIDANCE FOR AN AEROGRAVITY ASSISTED ATMOSPHERIC SAMPLE RETURN FROM MARS AND VENUS

Atmospheric lift can increase the bending and the net ∆V achievable during a gravity assist maneuver. This technique is applied to a detailed simulation of a hypothetical atmospheric sample return from Mars and Venus as part of an Earth-Mars-Venus-Earth tour. Reference flyby trajectories for both planets are computed for a waverider vehicle model. The bounding entry conditions are described and compared to those achievable using a recently developed guidance algorithm. for a detailed analysis of the issues associated with accurately guiding a waverider to the exit state required to target the next planet in the tour. Simplifying assumptions are used to generate a reference trajectory through the atmosphere of Mars. Unfortunately, using those same simplifications at Venus resulted in a trajectory that was biased toward one side of the achievable entry corridor. We will describe an approach based on optimal control theory for specifying a reference trajectory that is approximately centered in the available corridor for both Mars and Venus. The reference trajectories will be compared and contrasted for the cases at Mars and Venus. The theoretical bounding cases for Venus will be described and the bounds for Mars will be computed. Earlier papers 1-3,6-23 discuss the history of the waverider concept for interplanetary missions. A guidance algorithm has been developed and implemented in MATLAB. This paper will focus on using angle of attack as the control, while bank angle control and the guidance system development is discussed in a companion paper by Casoliva 2 . The guidance algorithms have been tested using simple models (point mass, spherical planets with exponential atmospheres). The MATLAB based guidance algorithm has been successfully linked into a DSENDS 24 based numerical simulation of the atmospheric flyby segments. DSENDS is a Python based simulator that enables the user to couple complex models of atmospheres, gravity, actuators, sensors, and control mechanisms in a common simulation environment. Monte Carlo simulations of the performance of the guidance algorithm are underway to determine the ability of the guidance algorithm to adapt to errors in entry conditions and to off-nominal atmospheric conditions.