Optimal Abort Guidance and Experimental Verification Based on Feature Learning.

The abort mission refers to the mission where the landing vehicle needs to terminate the landing mission when an anomaly happens and be safely guided to the desired orbit. This paper focuses on solving the time-optimal abort guidance (TOAG) problem in real-time via the feature-based learning method. First, according to the optimal control theory, the features are identified to represent the optimal solutions of TOAG using a few parameters. After that, a sufficiently large data set of time-optimal abort trajectories is generated offline by solving the TOAG problems with different initial conditions. Then, the features are extracted for all generated cases. To find the implicit relationships between the initial conditions and identified features, neural networks are constructed to map the relationships based on the generated data set. Finally, experimental flight tests are conducted to demonstrate the onboard computation capability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]