Stochastic Trajectory Planning for Autonomous Aerobraking Using Convex Optimization and Covariance Control

Abstract

Trajectory planning for autonomous aerobraking poses significant technical challenges, primarily due to the complexities associated with ensuring reliable onboard computation, mitigating flying uncertainty, enhancing aerodeceleration efficacy, and guaranteeing sufficient precision. To address this issue, this paper utilizes convex optimization for the first time to synchronously generate an anti-uncertainty trajectory and corresponding closed-loop controller with efficient, robust, and high-precision computational capability. Specifically, a double-loop planning framework is developed. In the inner loop, techniques of covariance control and linear covariance analysis are employed to give rise to a stochastic trajectory planning problem that optimizes the orbiter’s capacity to withstand and overcome deviations during flight. Furthermore, for onboard planning considerations, the problem’s nonlinearity is equivalently reduced by projecting the time-independent variable into the altitude variable. On this basis, a novel state-complete linearization method is introduced to enable the utilization of sequential convex programming with improved convergence. For the outer loop, the orbiter’s motion is decomposed into low-accuracy longitudinal and high-precision dynamics. A differential-correction-based technique is specifically designed to remedy the dynamics in turn. Numerical simulations demonstrate that the proposed method can reduce mission risk by at least 70% while only experiencing an additional cost of velocity impulse amounting to 20%.