Orbit-Injection Strategy and Trajectory-Planning Method of the Launch Vehicle under Power Failure Conditions

• Published in: Aerospace Vol. 9; no. 4; p. 199
• Main Authors: Diao, Yin, Pu, Jialun, Xu, Hechuan, Mu, Rongjun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2022
• Subjects: Accuracy; Convex analysis; Decision making; Decision theory; Failure; Failure analysis; Failure modes; Injection; launch vehicle; Methods; Neural networks; Numerical analysis; online trajectory planning; Optimization algorithms; orbit-injection strategy; Power; power failure; Satellite communications; Simulation analysis; Strategy; target degradation; Trajectory planning; Vehicles
• DOI: 10.3390/aerospace9040199
• ISSN: 2226-4310

Aiming at the problem of autonomous decision making and trajectory planning (ADMTP) for launch vehicles under power failure conditions, the target degradation order strategy (TDOS) and the trajectory online planning method were studied in this paper. Firstly, the influence of power failure on the orbit-injection process was analyzed. Secondly, the TDOS was proposed according to the mission attribute, failure mode, and multi-payload combination. Then, an online planning method based on the adaptive target update iterative guidance method (ATU-IGM) and radial basis neural network (RBFNN) was proposed, where the ATU-IGM adopted the basic TDOS criterion for generating optimal orbit-injection samples and online guidance instructions, and the RBFNN was used for orbit-injection samples training and online generation of orbital missions. Finally, the comparative simulation analysis was performed under multi-failure conditions. The results showed that the TDOS proposed in this paper could meet the requirements of the mission decision making under different failures, target orbit types, orbit-injection methods, and payload compositions. The online trajectory-planning capability deviation was less than 5%, and the mission decision-making and trajectory-planning time were less than 10 ms. This study provides theoretical support for autonomous decision making and planning of space launch missions.