Joint Trajectory Replanning for Mars Ascent Vehicle under Propulsion System Faults: A Suboptimal Learning-Based Warm-Start Approach

TL;DR

This paper introduces a suboptimal learning-based approach for rapid joint trajectory replanning of Mars ascent vehicles under propulsion faults, combining convex optimization with neural network warm-starts for improved efficiency and reliability.

Contribution

It develops a convex optimization framework for joint trajectory and orbit reoptimization and integrates a deep neural network warm-start scheme for real-time fault response.

Findings

Effective in simulation under thrust drop faults

Improves replanning speed and reliability

Demonstrates robustness across orbit types

Abstract

During the Mars ascent vehicle (MAV) launch missions, when encountering a thrust drop type of propulsion system fault problem, the general trajectory replanning methods relying on step-by-step judgments may fail to make timely decisions, potentially leading to mission failure. This paper proposes a suboptimal joint trajectory replanning (SJTR) method, which formulates the joint optimization problem of target orbit and flight trajectory after a fault within a convex optimization framework. By incorporating penalty coefficients for terminal constraints, the optimization solution adheres to the orbit redecision principle, thereby avoiding complex decision-making processes and resulting in a concise and rapid solution to the replanning problem. A learning-based warm-start scheme is proposed in conjunction with the designed SJTR method. Offline, a deep neural network (DNN) is trained using a dataset generated by the SJTR method. Online, the DNN provides initial guesses for the time optimization variables based on the current fault situation, enhancing the solving efficiency and reliability of the algorithm. Numerical simulations of the MAV flight scenario under the thrust drop faults are performed, and Monte Carlo experiments and case studies across all orbit types demonstrate the effectiveness of the proposed method.